Contexto empresarial. Analizaremos los datos de la Encuesta Permanente de Hogares, llevada a cabo por el INDEC (Instituto Nacional de Estadística y Censos). Las bases contienen una significativa cantidad de variables de hogar y personas para posibilitar el análisis de las principales características demográficas y socioeconómicas de la población argentina. Esta encuesta se realiza desde el año 2003, con frecuencia trimestral.
Problema comercial. Centraremos nuestro análisis en el salario de las personas. Únicamente emplearemos los datos correspondientes al dataset más reciente al momento de comenzar el trabajo (primer trimestre del año 2022), por dos razones. En primer lugar, para tener un volumen manejable de datos (de por sí esta sola base de datos tiene 177 columnas por más de 49 mil filas). Y en segundo lugar, porque nuestra intención es predecir el salario en base a una serie de variables que mencionaremos a continuación, pero sin tener en cuenta la inflación mensual/anual.
Contexto analítico. Se descargó el archivo Excel del sitio oficial del INDEC (https://www.indec.gob.ar/indec/web/Institucional-Indec-BasesDeDatos-1). En él, cada registro tiene un número de identicación (CODUSU), que permite relacionar una vivienda con los hogares y las personas que la componen.
Todos los miembros del hogar tienen el mismo CODUSU y NRO_HOGAR pero se diferencian por el número de COMPONENTE.
Dentro del contenido del enlace pegado arriba, también se encontrará la documentación donde se detalla el significado de cada columna del dataset.
Se realizarán las siguientes tareas:
Importación de librerías
import plotly
plotly.offline.init_notebook_mode(connected=True) #Para poder visualizar los gráficos de plotly en formato html
#Importación de librería de funciones matemáticas
import numpy as np
#Importación de librería de manipulación y análisis de datos
import pandas as pd
#Importación de librerías para elaboración de visualizaciones
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly import subplots
from plotly.subplots import make_subplots
import plotly.graph_objects as go
#Importación de librería para creación de iteradores
import itertools
#Para omitir mensajes de advertencia
import warnings
warnings.filterwarnings('ignore')
Instalación de paquetes de algoritmos de Machine Learning
Instalación del paquete de CatBoost
pip install catboost
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Installing collected packages: catboost
Successfully installed catboost-1.1.1
Instalación del paquete de LightGBM
pip install lightgbm
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Instalación del paquete de XGBoost
pip install xgboost
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Importación de algoritmos y métricas de evaluación
#Para eliminación de outliers
from sklearn.ensemble import IsolationForest
#Importación de Algoritmos
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
from catboost import CatBoostRegressor
import lightgbm as lgb
from xgboost import XGBRegressor
#Importación de Métricas
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
Importación del dataset
#Lectura de archivo excel
df= pd.read_excel('usu_individual_T122.xlsx')
df.head(5)
| CODUSU | ANO4 | TRIMESTRE | NRO_HOGAR | COMPONENTE | H15 | REGION | MAS_500 | AGLOMERADO | PONDERA | ... | PDECIFR | ADECIFR | IPCF | DECCFR | IDECCFR | RDECCFR | GDECCFR | PDECCFR | ADECCFR | PONDIH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TQRMNOQXQHLOKQCDEGKDB00777573 | 2022 | 1 | 1 | 2 | 1 | 43 | N | 14 | 104 | ... | 10.0 | 10 | 77500.0 | 9 | 9.0 | 9 | NaN | 9.0 | 10 | 194 |
| 1 | TQRMNOQXQHLOKQCDEGKDB00777573 | 2022 | 1 | 1 | 3 | 1 | 43 | N | 14 | 104 | ... | 10.0 | 10 | 77500.0 | 9 | 9.0 | 9 | NaN | 9.0 | 10 | 194 |
| 2 | TQRMNOQXQHLOKQCDEGKDB00777573 | 2022 | 1 | 1 | 4 | 1 | 43 | N | 14 | 104 | ... | 10.0 | 10 | 77500.0 | 9 | 9.0 | 9 | NaN | 9.0 | 10 | 194 |
| 3 | TQRMNOSUPHKKPQCDEIJAH00780151 | 2022 | 1 | 1 | 1 | 1 | 1 | S | 33 | 1741 | ... | NaN | 12 | 0.0 | 12 | NaN | 12 | 12.0 | NaN | 12 | 0 |
| 4 | TQRMNOSUPHKKPQCDEIJAH00780151 | 2022 | 1 | 1 | 2 | 1 | 1 | S | 33 | 1741 | ... | NaN | 12 | 0.0 | 12 | NaN | 12 | 12.0 | NaN | 12 | 0 |
5 rows × 177 columns
Tamaño del dataset
#Tamaño del dataset
df.shape
(49706, 177)
Vemos que nuestro conjunto de datos se compone de 49706 filas y 177 columnas.
Información sobre las columnas del dataset
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 49706 entries, 0 to 49705 Columns: 177 entries, CODUSU to PONDIH dtypes: float64(106), int64(66), object(5) memory usage: 67.1+ MB
De las 177 columnas, tenemos 106 en formato de número decimal, 66 en formato de número entero y 5 columnas en formato objeto.
Aplicaremos el método .describe() de Pandas para obtener información de las columnas numéricas.
describe= df.describe()
describe2= describe.transpose()
describe2
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| ANO4 | 49706.0 | 2022.000000 | 0.000000 | 2022.0 | 2022.00 | 2022.0 | 2022.0 | 2022.0 |
| TRIMESTRE | 49706.0 | 1.000000 | 0.000000 | 1.0 | 1.00 | 1.0 | 1.0 | 1.0 |
| NRO_HOGAR | 49706.0 | 1.041544 | 0.737641 | 1.0 | 1.00 | 1.0 | 1.0 | 52.0 |
| COMPONENTE | 49706.0 | 2.494387 | 1.620591 | 1.0 | 1.00 | 2.0 | 3.0 | 51.0 |
| H15 | 49706.0 | 0.871907 | 0.338266 | 0.0 | 1.00 | 1.0 | 1.0 | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| RDECCFR | 49706.0 | 6.300507 | 3.883657 | 0.0 | 3.00 | 6.0 | 10.0 | 12.0 |
| GDECCFR | 22971.0 | 6.555309 | 4.101544 | 0.0 | 3.00 | 6.0 | 12.0 | 12.0 |
| PDECCFR | 26735.0 | 5.980550 | 3.711121 | 0.0 | 3.00 | 5.0 | 9.0 | 12.0 |
| ADECCFR | 49706.0 | 6.324568 | 3.880445 | 0.0 | 3.00 | 6.0 | 10.0 | 12.0 |
| PONDIH | 49706.0 | 584.899147 | 1318.860078 | 0.0 | 90.25 | 221.0 | 464.0 | 26184.0 |
172 rows × 8 columns
Esta descripción de las columnas numéricas es únicamente a los fines de obtener una primera idea de la composición del dataset. Sin embargo, se debe tener en cuenta que dentro de las 49706 filas, se encuentran las personas que trabajan y las que no, por lo que posteriormente se realizará un filtro para obtener información solo de aquellas personas laboralmente activas.
Variables más relevantes para el análisis
Luego de leer detenidamente la documentación, las variables que consideramos más importantes para predecir los ingresos de los trabajadores argentinos son las siguientes:
Dentro de las variables seleccionadas, hay algunas que son muy similares entre sí, por ejemplo las relativas al nivel académico (NIVEL_ED y CH12), o algunas relativas a la situación ocupacional (PP04D_COD contiene información similar a CAT_OCUP).
Se realizará el EDA y el modelado en Machine Learning con todas, y luego se decidirá cuál conservar en el modelo definitivo.
Se elaborarán visualizaciones para intentar comprobar las siguientes hipótesis:
Existe mayor variabilidad en los ingresos de trabajadores independientes que para el caso de los trabajadores asalariados.
Para trabajadores asalariados, se evidencia una diferencia entre el salario percibido por varones y mujeres.
Existe variabilidad entre los salarios percibidos por los trabajadores asalariados, según la región del país, siendo que los que residen en capital y alrededores ganan más dinero que los que viven en el interior del país.
Aquellos trabajadores, ya sean asalariados o independientes, que alcanzaron un mayor nivel educativo, perciben mayores ingresos.
Aquellos trabajadores, ya sean asalariados o independientes, que tienen mayor edad, perciben mayores ingresos.
Sin diferenciar entre trabajadores asalariados o independientes, se evidencia que a mayor cantidad de horas trabajadas, mayores son los ingresos percibidos.
La intensidad de la ocupación de las personas influye en los ingresos percibidos.
Los ingresos de las personas dependen en gran medida de su calificación ocupacional y de la tecnología que emplean en su trabajo.
Las características de la compañía/negocio en el que trabajan las personas influyen en sus ingresos (si es ámbito público o privado, tamaño de la compañía, lugar físico de trabajo, actividad económica a la que se dedica).
Queremos indagar sobre la columna que contiene los ingresos de los trabajadores:
df['P21'].describe().round(0)
count 49706.0 mean 19199.0 std 39613.0 min -9.0 25% 0.0 50% 0.0 75% 30000.0 max 2000000.0 Name: P21, dtype: float64
Como vemos, el valor mínimo es -9, lo cual, según se aclaró más arriba, representa un código de no respuesta. Por lo tanto, eliminaremos las filas que contengan este valor.
ingresos_sin_especificar=df.P21[df['P21']==-9]
ingresos_sin_especificar.value_counts()
-9 3465 Name: P21, dtype: int64
Existen 3465 filas con ingresos sin especificar. A continuación, las eliminaremos.
df= df[df['P21']!=-9]
df.shape
(46241, 177)
Nos queda entonces un dataset reducido a 46241 filas.
Aplicando nuevamente el método describe a la columna de ingresos, solo teniendo en cuenta los valores mayores a cero:
df['P21'][df['P21']>0].describe().round(0)
count 17212.0 mean 55445.0 std 50220.0 min 200.0 25% 25000.0 50% 45000.0 75% 70000.0 max 2000000.0 Name: P21, dtype: float64
ingresos_de_trabajadores_en_dólares = (55445/116.51) #Se emplea el valor de cambio oficial a Marzo 2022
ingresos_de_trabajadores_en_dólares = round (ingresos_de_trabajadores_en_dólares,0)
print ('Ingreso promedio de los trabajadores argentinos en dólares: U$D '+str(ingresos_de_trabajadores_en_dólares))
Ingreso promedio de los trabajadores argentinos en dólares: U$D 476.0
Notamos que el ingreso promedio de los trabajadores, es $55445 pesos argentinos, unos 476 dólares estadounidenses al valor de cambio oficial de Marzo 2022, mes que corresponde a los salarios del dataset.
Crearemos un nuevo dataset que contenga solo las variables que consideramos relevantes, a fin de analizar su correlación con la variable objetivo: Ingresos (columna 'P21').
#Creamos un nuevo dataset que contenga solo las variables que consideramos relevantes
df_1=df[['REGION','AGLOMERADO','MAS_500','CH04','CH06','CH12','NIVEL_ED','ESTADO','CAT_OCUP','PP03D','PP3E_TOT','INTENSI','PP04A','PP04B_COD','PP04C','PP04D_COD','PP04G','P21']]
#Analizaremos la correlación entre las variables numéricas
correlations= df_1.corr()
indx=correlations.index
#Para visualizar la matriz de correlación
plt.figure(figsize=(16,12))
sns.heatmap(df_1[indx].corr(),annot=True,cmap="YlGnBu")
<AxesSubplot:>
A priori, sin ninguna transformación de las columnas del dataframe, la variable que más relación tendría con los ingresos "P21" sería la categoría ocupacional "CAT_OCUP", con un coeficiente de correlación igual a 0,6.
Veremos si esta primera impresión es cierta y qué insights obtendremos a medida que avanzamos en el EDA.
A continuación agruparemos las variables en numéricas o categóricas (y éstas a su vez en ordinales y no ordinales), dado que a posteriori cada una recibirá un tratamiento diferente.
Variables numéricas:
Variables categóricas no ordinales:
Variables categóricas ordinales:
Como parte de la etapa de limpieza y preparación de los datos para poder consumirlos en visualizaciones, lo primero que haremos será tratar los valores nulos.
df_1.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null int64 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 CH04 46241 non-null int64 4 CH06 46241 non-null int64 5 CH12 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 PP03D 17756 non-null float64 10 PP3E_TOT 17756 non-null float64 11 INTENSI 17756 non-null float64 12 PP04A 17756 non-null float64 13 PP04B_COD 17756 non-null float64 14 PP04C 17756 non-null float64 15 PP04D_COD 17756 non-null float64 16 PP04G 17756 non-null float64 17 P21 46241 non-null int64 dtypes: float64(8), int64(9), object(1) memory usage: 6.7+ MB
Dado que desdela columna de índice 9 hasta la 16 tenemos valores nulos, reemplazaremos los mismos por ceros.
df_1.fillna(0,inplace=True)
df_1.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null int64 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 CH04 46241 non-null int64 4 CH06 46241 non-null int64 5 CH12 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 PP03D 46241 non-null float64 10 PP3E_TOT 46241 non-null float64 11 INTENSI 46241 non-null float64 12 PP04A 46241 non-null float64 13 PP04B_COD 46241 non-null float64 14 PP04C 46241 non-null float64 15 PP04D_COD 46241 non-null float64 16 PP04G 46241 non-null float64 17 P21 46241 non-null int64 dtypes: float64(8), int64(9), object(1) memory usage: 6.7+ MB
Renombraremos las columnas de variables numéricas para facilitar la interpretación de los gráficos.
df_1 = df_1.rename(columns={'CH06':'Edad','PP3E_TOT': 'Horas_sem', 'P21':'Ingresos', 'PP03D':'Cant_Ocup'})
También tenombraremos algunas de las columnas de variables numéricas para facilitar la interpretación de las visualizaciones.
df_1 = df_1.rename(columns={'CH04':'Sexo','PP04D_COD':'Cod_Ocup','PP04A':'Tipo_empr','PP04B_COD': 'Cod_activ','PP04G':'Lugar_trab','CH12':'NIVEL_ED_2','PP04C':'Tamaño_empr'})
A continuación iremos traduciendo los códigos de las categorías a su significado en texto.
Dejaremos para el final las variables 'Cod_Ocup' y 'Cod_activ' ya que requieren transformaciones adicionales.
#Traducimos el código de la columna Sexo a su significado
df_1['Sexo']=df_1['Sexo'].replace({2:'F',1:'M'})
#Traducimos el código de la columna Región a su significado
df_1['REGION']=df_1['REGION'].replace({1:'GBA', 40:'NOA', 41:'NEA',
42:'CUYO', 43:'PAMPEANA', 44:'PATAGONIA'})
#Verificaremos que no exista el valor 99 correspondiente a la categoría Ns/Nr.
df_1['AGLOMERADO'].value_counts()
33 4340 23 2301 13 2150 29 2062 10 1999 27 1861 19 1781 4 1710 18 1677 22 1476 25 1428 32 1407 38 1404 14 1350 15 1347 26 1296 8 1292 91 1242 5 1215 36 1191 7 1140 6 1123 17 1096 12 1073 93 1036 31 1035 9 983 2 962 3 860 34 823 20 819 30 762 Name: AGLOMERADO, dtype: int64
Dado que no hay valores de código de No Respuesta, por el momento dejaremos los valores de la columna Aglomerado tal como están. La única transformación que haremos será convertir la variable de tipo número decimal a número entero.
#Convertimos la columna 'AGLOMERADO'a tipo entero.
df_1['AGLOMERADO']=df_1['AGLOMERADO'].astype(int)
Por el momento no se requiere transformación de esta variable.
#Traducimos el código de la columna Tipo_empr a su significado
df_1['Tipo_empr']=df_1['Tipo_empr'].replace({1:'Estatal', 2:'Privada', 3:'Otro'})
#Verificaremos que no exista el valor 99 correspondiente a la categoría Ns/Nr.
df_1['Lugar_trab'].value_counts()
0.0 29721 1.0 10830 8.0 1823 6.0 1236 4.0 809 9.0 756 5.0 628 10.0 217 3.0 115 7.0 68 2.0 38 Name: Lugar_trab, dtype: int64
Dado que no hay valores de código de No Respuesta, por el momento dejaremos los valores de la columna 'Lugar físico de trabajo' tal como están. La única transformación que haremos será convertir la variable de tipo número decimal a número entero.
#Convertimos la columna 'Lugar_trab'a tipo entero.
df_1['Lugar_trab']=df_1['Lugar_trab'].astype(int)
#Traducimos el código de la columna Nivel educativo a su significado
df_1['NIVEL_ED']=df_1['NIVEL_ED'].replace({9:'0-Ns/Nr',1:'1-Primario incompl',2:'2-Primario compl',
3:'3-Secundario incompl',4:'4-Secundario compl',5:'5-Superior universit incompl',
6:'6-Superior universit compl',7:'0-Sin instrucción'})
#Traducimos el código de la columna Nivel educativo a su significado
df_1['NIVEL_ED_2']=df_1['NIVEL_ED_2'].replace({9:'0-Educación especial', 0:'1-Jardín/preescolar/Sin instr.', 1:'1-Jardín/preescolar/Sin instr.',2:'2-Primario',
3:'3-EGB',4:'4-Secundario',5:'5-Polimodal',
6:'6-Terciario',7:'7-Universitario',8:'8-Posgrado universitario'})
df_1['NIVEL_ED_2'].value_counts()
4-Secundario 18022 2-Primario 11659 7-Universitario 6560 6-Terciario 4383 1-Jardín/preescolar/Sin instr. 3629 3-EGB 855 5-Polimodal 586 0-Educación especial 274 8-Posgrado universitario 269 99 4 Name: NIVEL_ED_2, dtype: int64
#Reemplazamos los valores 2, 3 y 4 por cero, ya que no perciben ingresos
df_1['ESTADO']=df_1['ESTADO'].replace({2:0, 3:0, 4:0})
#Traducimos el código de la columna Categoría Ocupacional a su significado
df_1['CAT_OCUP']=df_1['CAT_OCUP'].replace({9:'Ns/Nr',1:'Patron',2:'Cuenta Propia',3:'Obrero o empleado',4:'Trabajador fliar s/remun'})
#Traducimos el código de la columna Intensidad de ocupación a su significado
df_1['INTENSI']=df_1['INTENSI'].replace({9:'Ns/Nr',1:'1-Subocupado',2:'2-Ocupado pleno',3:'3-Sobreocupado',4:'2-Ocup q no trabajó ult sem'})
#Traducimos el código de la columna Tamaño_empr a su significado
df_1['Tamaño_empr']=df_1['Tamaño_empr'].replace({1:'1) 1 pers',2:'2) 2 pers',3:'3) 3 pers',4:'4) 4 pers',5:'5) 5 pers',
6:'6) 6 a 10 pers', 7:'7) 11 a 25 pers', 8:'8) 26 a 40 pers', 9:'9) 41 a 100 pers',
10:'10) 101 a 200 pers', 11:'11) 201 a 500 pers', 12:'12) Más de 500 pers', 0:'0) Ns/Nr', 99:'0) Ns/Nr'})
A los fines de reducir la cantidad de categorías de la variable Tamaño_empr, crearemos una nueva variable Tamaño_empr_2, donde clasificaremos las empresas según el siguiente criterio:
1) Microempresas: cuando tengan una planta de personal de hasta CINCO (5) trabajadores;
2) Pequeñas: cuando tengan una planta de personal de SEIS (6) a CUARENTA (40) trabajadores;
3) Medianas: cuando tengan una planta de personal de CUARENTA Y UN (41) a DOSCIENTOS (200) trabajadores;
4) Grandes: cuando tengan una planta de personal de más de DOSCIENTOS (200) trabajadores.
#Creamos la columna Tamaño_empr_2 a partir de la columna Tamaño_empr
df_1['Tamaño_empr_2']=df_1['Tamaño_empr'].replace({'1) 1 pers': 'Microempresa','2) 2 pers':'Microempresa','3) 3 pers':'Microempresa','4) 4 pers':'Microempresa',
'5) 5 pers':'Microempresa', '6) 6 a 10 pers':'Pequeña',
'7) 11 a 25 pers':'Pequeña', '8) 26 a 40 pers':'Pequeña', '9) 41 a 100 pers':'Mediana',
'10) 101 a 200 pers':'Mediana','11) 201 a 500 pers':'Grande', '12) Más de 500 pers':'Grande'})
Esta variable, originariamente denominada 'PP04D_COD', y que renombramos como 'Cod_Ocup', es un código compuesto por 5 dígitos, donde cada dígito representa una categoría en sí, tal como se muestra debajo.
Plantearemos el análisis de dos formas:
Por un lado, conservando la columna tal y como está, teniendo presente que, a pesar de que sus datos son números, se trata de una variable categórica.
Por otro lado, crearemos cuatro nuevas variables a partir de la variable original.
df_1['Cod_Ocup'].value_counts()
0.0 28485
10333.0 1263
55314.0 900
56314.0 744
30113.0 716
...
34203.0 1
40319.0 1
20311.0 1
46112.0 1
62312.0 1
Name: Cod_Ocup, Length: 435, dtype: int64
Vemos que, hay 28485 personas que no completaron este campo.
#Convertimos la columna 'Cod_Ocup'a tipo entero.
df_1['Cod_Ocup']=df_1['Cod_Ocup'].astype(int)
#Creamos una columna igual a 'Cod_Ocup' pero de tipo string
df_1['ocupacion']=df_1['Cod_Ocup'].astype(str)
ocupacion= df_1['ocupacion']
#Creamos la columna Caracter Ocupacional
#Extraemos los dos primeros caracteres y los volcamos en una nueva columna del dataset
df_1['CARACTER_OCUP']=ocupacion.str[-5:-3]
#Creamos la columna Jerarquia Ocupacional
#Extraemos el tercer caracter y lo volcamos en una nueva columna del dataset
df_1['JERARQUIA_OCUP']=ocupacion.str[-3:-2]
#Creamos la columna Tecnologia Ocupacional
#Extraemos el cuarto caracter y lo volcamos en una nueva columna del dataset
df_1['TECNOLOGIA_OCUP']=ocupacion.str[-2:-1]
#Creamos la columna Calificacion Ocupacional
#Extraemos el quinto caracter y lo volcamos en una nueva columna del dataset
df_1['CALIFICACION_OCUP']=ocupacion.str[-1:]
df_1['CARACTER_OCUP'].nunique()
52
En nuestro dataset hay 52 categorías diferentes para la variable Carácter ocupacional. Las analizaremos más adelante.
#Traducimos el código de la columna Jerarquía ocupacional a su significado
df_1['JERARQUIA_OCUP']=df_1['JERARQUIA_OCUP'].replace({'0':'Direccion','1':'Cuenta propia','2':'Jefes','3':'Asalariados','9':'Ns/Nr'})
#Verificamos que los valores se hayan reemplazado correctamente
df_1['JERARQUIA_OCUP'].value_counts()
28487 Asalariados 12831 Cuenta propia 3723 Direccion 736 Jefes 383 Ns/Nr 81 Name: JERARQUIA_OCUP, dtype: int64
df_1['TECNOLOGIA_OCUP'].nunique()
6
Nos figuran 6 categorías, en lugar de 3. Veamos por qué:
df_1['TECNOLOGIA_OCUP'].value_counts()
28487 1 10400 3 4612 2 1542 0 1119 9 81 Name: TECNOLOGIA_OCUP, dtype: int64
#Traducimos el código de la columna Tecnología ocupacional a su significado
df_1['TECNOLOGIA_OCUP']=df_1['TECNOLOGIA_OCUP'].replace({'0':'Ns/Nr','9':'Ns/Nr','1':'Sin op. máq.','2':'Con op. máq.','3':'Op. sist. informatizados'})
#Reemplazamos todas las celdas vacías de las columnas nuevas por ceros
df_1['CARACTER_OCUP']=df_1['CARACTER_OCUP'].replace({'':0})
df_1['JERARQUIA_OCUP']=df_1['JERARQUIA_OCUP'].replace({'':0})
df_1['TECNOLOGIA_OCUP']=df_1['TECNOLOGIA_OCUP'].replace({'':0})
df_1['CALIFICACION_OCUP'].value_counts()
0 28485 3 9299 4 3807 2 3175 1 1390 7 71 9 8 8 6 Name: CALIFICACION_OCUP, dtype: int64
#Traducimos el código de la columna Calificación ocupacional a su significado
df_1['CALIFICACION_OCUP']=df_1['CALIFICACION_OCUP'].replace({'1':'Profesionales','2':'Técnicos','3':'Operativo','4':'No calificado', '7':'Ns/Nr','8':'Ns/Nr','9':'Ns/Nr'})
#Eliminamos la columna auxiliar "ocupacion", anteriormente creada
df_1=df_1.drop(['ocupacion'], axis=1)
Esta variable, originariamente denominada 'PP04B_COD', y que renombramos como 'Cod_activ', es un código compuesto por 4 dígitos, donde los dos primeros dígitos indican una categoría y los dos siguientes una subcategoría.
Para el análisis, extraeremos los dos primeros dígitos y crearemos una nueva variable a denominar 'ACTIV_ECON'. Para evitar un nivel de detalle excesivo, los siguientes dos dígitos se ignorarán.
Luego, agruparemos las categorías de la columna 'ACTIV_ECON' en otras más globales, y volcaremos esta información en una nueva variable a denominar 'CAT_ECON' (identificada por un código alfabético).
En la tabla seguiente se detallan las actividades económicas que emplea la encuesta:
#Queremos extraer las dos primeras cifras del código Cod_activ, por lo que primero lo convertimos a entero
df_1['Cod_activ']= df_1['Cod_activ'].astype(int)
#Reemplazamos el código 9999 (que significa actividad vacía o mal especificada) por cero
df_1['Cod_activ']=df_1['Cod_activ'].replace({9999:0})
#Convertimos la variable actividad_economica a tipo string, para extraer los dos dígitos de la izquierda
actividad_economica= df_1['Cod_activ'].astype(str)
#Creación de la variable 'ACTIV_ECON'
#Para ello extraemos los dos primeros caracteres y los volcamos en una nueva columna del dataset
df_1['ACTIV_ECON']=actividad_economica.str[:2]
df_1['ACTIV_ECON'].value_counts()
0 28639
48 2799
84 2298
40 1792
85 1541
...
50 5
39 1
99 1
12 1
37 1
Name: ACTIV_ECON, Length: 78, dtype: int64
#Asignaremos un código alfabético a cada grupo de actividades económicas, según la tabla del punto 14)
df_1['CAT_ECON']=df_1['ACTIV_ECON'].replace({
'1':'A','01':'A','02':'A','03':'A',
'05':'B','06':'B','07':'B','08':'B','09':'B',
'10':'C','11':'C','12':'C','13':'C',
'14':'C','15':'C','16':'C','17':'C','18':'C',
'19':'C','20':'C','21':'C','22':'C','23':'C',
'24':'C','25':'C','26':'C','27':'C','28':'C',
'29':'C','30':'C','31':'C','32':'C','33':'C',
'35':'D',
'36':'E','37':'E','38':'E','39':'E',
'40':'F',
'45':'G','48':'G',
'49':'H','50':'H','51':'H','52':'H','53':'H',
'55':'I','56':'I',
'58':'J','59':'J','60':'J','61':'J','62':'J','63':'J',
'64':'K','65':'K','66':'K',
'68':'L',
'69':'M','70':'M','71':'M','72':'M','73':'M','74':'M','75':'M',
'77':'N','78':'N','79':'N','80':'N','81':'N','82':'N',
'83':'O','84':'O',
'85':'P',
'86':'Q','87':'Q','88':'Q',
'90':'R','91':'R','92':'R','93':'R',
'94':'S','95':'S','96':'S',
'97':'T','98':'T',
'99':'U',
'0':'W','00':'W'
})
Veremos cómo quedaron nuestras nuevas variables:
df_1.head()
| REGION | AGLOMERADO | MAS_500 | Sexo | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | ... | Cod_Ocup | Lugar_trab | Ingresos | Tamaño_empr_2 | CARACTER_OCUP | JERARQUIA_OCUP | TECNOLOGIA_OCUP | CALIFICACION_OCUP | ACTIV_ECON | CAT_ECON | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | PAMPEANA | 14 | N | F | 42 | 6-Terciario | 6-Superior universit compl | 1 | Obrero o empleado | 0.0 | ... | 48311 | 1 | 150000 | Pequeña | 48 | Asalariados | Sin op. máq. | Profesionales | 84 | O |
| 1 | PAMPEANA | 14 | N | F | 19 | 6-Terciario | 5-Superior universit incompl | 1 | Obrero o empleado | 0.0 | ... | 30333 | 1 | 10000 | Microempresa | 30 | Asalariados | Op. sist. informatizados | Operativo | 48 | G |
| 2 | PAMPEANA | 14 | N | M | 13 | 4-Secundario | 3-Secundario incompl | 0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0) Ns/Nr | 0 | 0 | 0 | 0 | 0 | W |
| 4 | GBA | 33 | S | M | 68 | 4-Secundario | 4-Secundario compl | 0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0) Ns/Nr | 0 | 0 | 0 | 0 | 0 | W |
| 6 | PAMPEANA | 30 | N | M | 52 | 6-Terciario | 6-Superior universit compl | 1 | Obrero o empleado | 0.0 | ... | 41332 | 1 | 80000 | 0) Ns/Nr | 41 | Asalariados | Op. sist. informatizados | Técnicos | 85 | P |
5 rows × 25 columns
df_1['Cant_Ocup'].value_counts()
0.0 44748 2.0 1299 3.0 130 4.0 31 5.0 22 6.0 6 7.0 3 9.0 2 Name: Cant_Ocup, dtype: int64
Asumiremos, por lo que indica la documentación, que el valor 9 corresponde a la categoría No sabe/No responde, y lo reemplazaremos por cero.
df_1['Cant_Ocup']=df_1['Cant_Ocup'].replace({9:0})
Notar que está ausente el valor 1, es decir que el campo quedó en valor cero para aquellas personas que tienen una sola ocupación. Le asignaremos el valor 1 a quienes tengan valor cero pero que perciben ingresos por trabajar.
#Reemplazamos por 1 aquellas personas que perciben ingresos por trabajar pero que no completaron el campo cantidad de ocupaciones.
df_1['Cant_Ocup'] = np.where((df_1['Cant_Ocup'] <2 ) & (df_1['Ingresos'] > 0), 1, df_1['Cant_Ocup'])
#Verificamos que los valores se hayan reemplazado correctamente
df_1['Cant_Ocup'].value_counts()
0.0 28989 1.0 15761 2.0 1299 3.0 130 4.0 31 5.0 22 6.0 6 7.0 3 Name: Cant_Ocup, dtype: int64
#Convertimos la columna a tipo número entero
df_1['Cant_Ocup']=df_1['Cant_Ocup'].astype(int)
Antes de proseguir con el EDA, guardamos una copia del dataframe tal como está ahora, la cual necesitaremos más adelante, en la etapa de modelado en Machine Learning.
df_1a= df_1.copy()
A fin de analizar los valores de ingresos de la población económicamente activa, tomaremos la porción que tiene entre 18 años, valor que representa la mayoría de edad, y 65 años, edad jubilatoria para los hombres (en el caso de las mujeres es 60).
#Filtramos por edad para conservar las personas de 18 años o más
df_1=df_1[df_1['Edad']>=18]
#Filtramos por edad para conservar las personas de 65 años o menos
df_1=df_1[df_1['Edad']<=65]
Crearemos una nueva variable categórica no ordinal, a partir de la columna 'Edad', para tener las edades diferenciadas en rangos.
#Creamos una nueva columna "Edad_2" que agrupa a las personas por rango de edad
df_1['Edad_2'] = pd.qcut(df_1['Edad'], 6)
df_1['Edad_2'].value_counts()
(17.999, 24.0] 5268 (38.0, 46.0] 4704 (30.0, 38.0] 4699 (55.0, 65.0] 4526 (46.0, 55.0] 4328 (24.0, 30.0] 4161 Name: Edad_2, dtype: int64
Ahora que ya realizamos la limpieza de los datos y los preparamos para las visualizaciones, procederemos con el análisis univariado.
Éste consiste en el análisis de cada una de las variables estudiadas por separado, se basa exclusivamente en una única variable. Es el análisis más básico y por ende más primario. Se considera un análisis de tipo descriptivo y no relacional o de causalidad. Las técnicas más frecuentes de análisis univariado son la distribución de frecuencias para una tabla univariada y el análisis de las medidas de tendencia central de la variable (media, mediana, moda, varianza , desviación estándar, cuartiles entre otros).
Diferenciaremos el análisis según se trate de variables numéricas o categóricas.
#Agruparemos en una variable 'var_num' todas las features numéricas
var_num = ['Edad','Horas_sem','Ingresos']
Antes de graficar, eliminaremos dos outliers en particular que ensucian las visualizaciones:
Además, para las visualizaciones tendremos en cuenta solo aquellas personas con ingresos mayores a cero, es decir, aquellas que trabajan.
df_1=df_1[df_1['Ingresos']!=2000000]
df_1=df_1[df_1['Horas_sem']!=999]
df_1=df_1[df_1['Ingresos']>0]
A continuación realizaremos un gráfico de distribución y un boxplot para cada una de las variables numéricas.
#Creamos la función
def plot_num_vars(df_1,colname):
fig, axes = plt.subplots(1,2, figsize=(20,8))
#grafico de distribucion
plot00 = sns.distplot(df_1[colname], ax=axes[0])
axes[0].set_title('Distribution of {name}'.format(name=colname))
#boxplot
plot01 = sns.boxplot(df_1[colname], ax=axes[1])
axes[1].set_title('Boxplot of {nombre}'.format(nombre=colname))
plt.show()
#Graficamos
for i in var_num:
plot_num_vars(df_1,i)
A continuación realizaremos diagramas de boxplot para las variables Edad y Horas semanales trabajadas, diferenciando por sexo y por categoría ocupacional (recordemos que esta última categoría, en principio, es la que mayor correlación tiene con los ingresos).
fig = px.box(df_1,
x="Edad",
color= 'Sexo',
title= 'Edad según sexo'
)
fig.update_layout(
height = 500,
width = 500,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
xaxis_title = 'Edad de los trabajadores'
)
fig.show()
df_1.groupby(['Sexo']).agg({'Edad':['mean','median']}).reset_index()
| Sexo | Edad | ||
|---|---|---|---|
| mean | median | ||
| 0 | F | 40.382077 | 40.0 |
| 1 | M | 39.749946 | 39.0 |
fig = px.box(df_1,
x="Edad",
color= 'CAT_OCUP',
title= 'Edad según categoría ocupacional'
)
fig.update_layout(
height = 500,
width = 800,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
xaxis_title = 'Edad de los trabajadores'
)
fig.show()
df_1.groupby(['CAT_OCUP']).agg({'Edad':['mean','median']}).reset_index()
| CAT_OCUP | Edad | ||
|---|---|---|---|
| mean | median | ||
| 0 | Cuenta Propia | 42.196154 | 42.0 |
| 1 | Obrero o empleado | 39.283410 | 39.0 |
| 2 | Patron | 44.910828 | 46.0 |
fig = px.box(df_1,
x="Horas_sem",
color= 'Sexo',
title= 'Horas semanales trabajadas según sexo'
)
fig.update_layout(
height = 500,
width = 700,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
xaxis_title = 'Horas semanales trabajadas'
)
fig.show()
df_1.groupby(['Sexo']).agg({'Horas_sem':['mean','median']}).reset_index()
| Sexo | Horas_sem | ||
|---|---|---|---|
| mean | median | ||
| 0 | F | 28.087202 | 30.0 |
| 1 | M | 38.132394 | 40.0 |
Analizando la distribución del dataset según sexo, se observa que, en promedio, los hombres trabajan, por semana, 10 horas más que las mujeres (Se debe tener presente que estamos analizando el trabajo remunerado. No se incluyen las horas de trabajo no remunerado relativo a tareas domésticas o de cuidado de familiares).
fig = px.box(df_1,
x="Horas_sem",
color= 'CAT_OCUP',
title= 'Horas semanales trabajadas según categoría ocupacional'
)
fig.update_layout(
height = 500,
width = 800,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
xaxis_title = 'Horas semanales trabajadas'
)
fig.show()
df_1.groupby(['CAT_OCUP']).agg({'Horas_sem':['mean','median']}).reset_index()
| CAT_OCUP | Horas_sem | ||
|---|---|---|---|
| mean | median | ||
| 0 | Cuenta Propia | 35.495858 | 36.0 |
| 1 | Obrero o empleado | 32.799657 | 36.0 |
| 2 | Patron | 43.375796 | 44.0 |
No se detectan diferencias significativas entre asalariados e independientes (no patrones), en cuanto a la cantidad de horas trabajadas, siendo que en promedio ambas categorías trabajan alrededor de 36 horas a la semana. Por su parte, los patrones trabajan 8 horas más a la semana que las categorías anteriores.
Dado que se trata de la variable objetivo, esta variable será caracterizada en el análisis bivariado, contrastándola con cada una de las demás variables. Sin embargo, aquí adelantamos sus principales parámetros estadísticos para el rango de edades definido para el EDA.
df_1['Ingresos'].describe().round(0)
count 16684.0 mean 55563.0 std 47893.0 min 200.0 25% 26000.0 50% 45000.0 75% 70000.0 max 1000000.0 Name: Ingresos, dtype: float64
ingresos_de_trabajadores_en_dólares = (55563/116.51) #Se emplea el valor de cambio oficial a Marzo 2022
ingresos_de_trabajadores_en_dólares = round (ingresos_de_trabajadores_en_dólares,0)
print ('Ingreso promedio de los trabajadores argentinos en dólares (18 a 65 años): U$D '+str(ingresos_de_trabajadores_en_dólares))
Ingreso promedio de los trabajadores argentinos en dólares (18 a 65 años): U$D 477.0
Interpretación de los gráficos univariados- variables numéricas:
Analizando la distribución del dataset según sexo, se observa que, en promedio, los hombres trabajan 10 horas más por semana que las mujeres (Se debe tener presente que estamos analizando el trabajo remunerado. No se incluyen las horas de trabajo no remunerado relativo a tareas domésticas o de cuidado de familiares).
No se detectan diferencias significativas entre trabajadores por cuenta propia y empleados, en cuanto a la cantidad de horas trabajadas, siendo el valor medio para ambas categorías igual a 36 horas a la semana.
Quienes son patrones trabajan en promedio más horas a la semana que el resto de los trabajadores (44 horas, lo que se podría interpretar como una jornada laboral de 8 hs de lunes a viernes y 4 hs los sábados), y su edad media también es mayor (46 años).
El ingreso promedio de los trabajadores, para el rango de edades de 18 a 65 años, es $55563 pesos argentinos, unos 477 dólares estadounidenses al valor de cambio oficial de Marzo 2022, mes que corresponde a los salarios del dataset.
Si tomamos el valor medio, que no se ve afectado por los outliers, el ingreso de los trabajadores argentinos desciende a $45mil (386 dólares estadounidenses).
El 75% de los trabajadores argentinos gana $70mil (600 dólares estadounidenses) o menos.
fig = px.pie(df_1, names='Sexo', title='Composición de los trabajadores según sexo', color_discrete_sequence=['#C42021','#6C0E23'])
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
height = 500,
width = 500,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.95,
'x':0.5},
showlegend=False,
xaxis = {'categoryorder':'category ascending'}
)
fig.show()
Vemos que en cuestión de sexo el dataset se encuentra bastante balanceado, siendo que las mujeres representar casi el 45% de los trabajadores.
df_1['REGION'].value_counts(normalize=True)
PAMPEANA 0.270379 NOA 0.255334 PATAGONIA 0.142412 CUYO 0.117538 GBA 0.110705 NEA 0.103632 Name: REGION, dtype: float64
fig= px.histogram(df_1,
y='REGION',
text_auto='.2s',
color_discrete_sequence=['#542344'])
fig.update_traces(
textposition = 'outside',
)
fig.update_layout(
height = 700,
width = 900,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Región de residencia',
xaxis_title = 'Recuento de personas')
fig.show()
A continuación se visualiza la composición del dataset según región del país, expresada en porcentaje, en el siguiente mapa elaborado en QGIS:
Vemos que la mayor cantidad de personas del dataset viven en la región Pampeana, que incluye las provincias de La Pampa, Córdoba, Santa Fe, Entre Ríos y Buenos Aires, descontando la zona de Gran Buenos Aires (que abarca la Ciudad Autónoma de Buenos Aires y alrededores).
Convertiremos la columna 'AGLOMERADO' de tipo número entero a tipo texto, para facilitar su interpretación en el gráfico.
#Convertimos la columna 'AGLOMERADO'a tipo string
df_1['AGLOMERADO']=df_1['AGLOMERADO'].astype(str)
#Verificamos que se haya convertido correctamente
df_1['AGLOMERADO'].value_counts()
33 1305 23 927 13 844 19 757 10 752 29 742 27 728 18 663 4 623 25 593 22 578 32 542 91 495 36 486 26 481 8 472 38 431 31 428 15 428 12 419 17 413 7 410 5 389 14 388 9 375 93 358 6 340 20 307 30 264 34 262 2 254 3 230 Name: AGLOMERADO, dtype: int64
Graficamos, ordenando los aglomerados de mayor a menor, en función de la cantidad de encuestados:
fig= px.histogram(df_1,
y='AGLOMERADO',
text_auto='.2s',
color='MAS_500')
fig.update_layout(
height = 800,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Aglomerado urbano de residencia',
xaxis_title = 'Recuento de personas')
fig.show()
El aglomerado que cuenta con mayor cantidad de trabajadores encuestados es el 33=Partidos del GBA, perteneciente a la región del Gran Buenos Aires, con 1305 personas, seguido por el 23=Gran Salta, de la región del NOA (Noroeste Argentino), con 930 encuestados.
Por otro lado, el aglomerado con menor cantidad de encuestados es 03=Bahía Blanca- Cerri, de la región Pampeana, con 230 personas.
En el gráfico se diferencian los aglomerados por color, en función de si tienen más de 500 mil habitantes o no.
df_1['Tipo_empr'].value_counts()
Privada 12164 Estatal 4268 Otro 252 Name: Tipo_empr, dtype: int64
fig = px.pie(df_1, names='Tipo_empr', title='Composición de los trabajadores según tipo de empresa/negocio', color_discrete_sequence=['#C42021','#6C0E23','#D9828B'])
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
height = 500,
width = 700,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.95,
'x':0.5},
showlegend=False,
xaxis = {'categoryorder':'category ascending'}
)
fig.show()
Vemos que casi 3 de cada 4 trabajadores encuestados trabajan en empresas de ámbito privado.
Convertiremos la columna 'Lugar_trab' de tipo número entero a tipo texto, para facilitar su interpretación en el gráfico.
#Convertimos la columna 'Lugar_trab'a tipo entero.
df_1['Lugar_trab']=df_1['Lugar_trab'].astype(str)
df_1['Lugar_trab'].value_counts(normalize=True)
1 0.614301 8 0.102074 0 0.069947 6 0.065931 4 0.045553 9 0.043215 5 0.035004 10 0.012047 3 0.006593 7 0.003476 2 0.001858 Name: Lugar_trab, dtype: float64
fig= px.histogram(df_1,
y='Lugar_trab',
text_auto='.2s',
color_discrete_sequence=['#542344'])
fig.update_layout(
height = 800,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Lugar físico de trabajo',
xaxis_title = 'Recuento de personas')
fig.show()
Del gráfico observamos que el 60% de los trabajadores del dataset tienen como lugar físico de trabajo la categoría 1="Local/oficina/establecimiento/negocio/taller/chacra/finca", seguido por las categorías 8="Domicilio/local de los clientes" (10% de los trabajadores) y categoría 6="En su vivienda- sin lugar exclusivo" (6,5% de los trabajadores). No se tiene en cuenta la categoría "0" que representa la no respuesta del encuestado.
df_1['NIVEL_ED'].value_counts(normalize=True)
4-Secundario compl 0.306162 6-Superior universit compl 0.229741 3-Secundario incompl 0.177236 5-Superior universit incompl 0.135879 2-Primario compl 0.118317 1-Primario incompl 0.030568 0-Sin instrucción 0.002098 Name: NIVEL_ED, dtype: float64
fig= px.histogram(df_1,
y='NIVEL_ED',
text_auto='.2s',
color_discrete_sequence=['#542344'])
fig.update_layout(
height = 600,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Máximo nivel académico alcanzado',
xaxis_title = 'Recuento de personas')
fig.show()
Se observa que el 30% de los trabajadores finalizaron el secundario. En segundo lugar se encuentran aquellos con estudios universitarios completos, que componen casi el 23% del dataset.
df_1['NIVEL_ED_2'].value_counts(normalize=True)
4-Secundario 0.456725 7-Universitario 0.201031 6-Terciario 0.152481 2-Primario 0.143431 5-Polimodal 0.018401 3-EGB 0.013546 8-Posgrado universitario 0.012047 1-Jardín/preescolar/Sin instr. 0.002038 0-Educación especial 0.000300 Name: NIVEL_ED_2, dtype: float64
fig= px.histogram(df_1,
y='NIVEL_ED_2',
text_auto='.2s',
color_discrete_sequence=['#542344'])
fig.update_layout(
height = 600,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Máximo nivel académico alcanzado-2',
xaxis_title = 'Recuento de personas')
fig.show()
Si bien esta variable es similar a la anterior, resulta interesante porque incluye la categoría de posgrado, ausente en la variable NIVEL_ED, y además diferencia el nivel de educación superior entre terciario y universitario. Se observa que casi la mitad de los trabajadores del dataset alcanzaron como nivel académico máximo el secundario (completo o incompleto), duplicando incluso a la categoría que le sigue, que son los de nivel universitario. Notar además el bajo porcentaje de personas con estudios de posgrado (aprox. 1,2%).
df_1['ESTADO'].value_counts()
1 16684 Name: ESTADO, dtype: int64
En la variable 'Estado', es correcto que todos los valores pertenezcan a la categoría 1 que corresponde a "Ocupado", ya que previamente se seleccionaron para el EDA aquellas personas que trabajan. Esta columna se incluirá en el modelado en Machine Learning para definir cuán relevante resulta.
df_1['CAT_OCUP'].value_counts()
Obrero o empleado 12833 Cuenta Propia 3380 Patron 471 Name: CAT_OCUP, dtype: int64
df_1['CAT_OCUP'].value_counts(normalize=True)
Obrero o empleado 0.769180 Cuenta Propia 0.202589 Patron 0.028231 Name: CAT_OCUP, dtype: float64
df_1.Sexo.groupby(df_1['CAT_OCUP']).value_counts(normalize=True)
CAT_OCUP Sexo
Cuenta Propia M 0.594083
F 0.405917
Obrero o empleado M 0.536118
F 0.463882
Patron M 0.726115
F 0.273885
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
x='CAT_OCUP',
text_auto='.2s',
color='Sexo')
fig.update_traces(
textposition = 'outside',
)
fig.update_layout(
height = 500,
width = 500,
xaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Categoría ocupacional',
yaxis_title = 'Recuento de personas',
title_text="CATEGORÍA OCUPACIONAL SEGÚN SEXO")
fig.show()
Vemos que en cuanto a la categoría ocupacional, el 77% del dataset está compuesto por trabajadores asalariados (obreros o empleados). Le siguen los trabajadores intependientes o cuenta propia, con un 20%. Finalmente, el menor porcentaje lo tienen los patrones (equivalen a menos del 3% del conjunto de datos).
Analizando la distribución por sexo, se da que para todas las categorías hay mayor cantidad de hombres que mujeres. La situación más desigual se da para el caso de los patrones, donde el 72% de ellos son varones.
df_1['INTENSI'].value_counts()
2-Ocupado pleno 8784 3-Sobreocupado 4605 1-Subocupado 1720 2-Ocup q no trabajó ult sem 1575 Name: INTENSI, dtype: int64
df_1['INTENSI'].value_counts(normalize=True)
2-Ocupado pleno 0.526492 3-Sobreocupado 0.276013 1-Subocupado 0.103093 2-Ocup q no trabajó ult sem 0.094402 Name: INTENSI, dtype: float64
df_1.Sexo.groupby(df_1['INTENSI']).value_counts(normalize=True)
INTENSI Sexo
1-Subocupado F 0.541860
M 0.458140
2-Ocup q no trabajó ult sem F 0.587937
M 0.412063
2-Ocupado pleno M 0.522655
F 0.477345
3-Sobreocupado M 0.695331
F 0.304669
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
x='INTENSI',
text_auto='.2s',
color='Sexo')
fig.update_traces(
textposition = 'outside',
)
fig.update_layout(
height = 500,
width = 600,
xaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Categoría ocupacional',
yaxis_title = 'Recuento de personas',
title_text="INTENSIDAD OCUPACIONAL SEGÚN SEXO")
fig.show()
Más de la mitad de los trabajadores encuestados declara ser ocupado pleno (el porcentaje asciende a 62% si incluimos los ocupados que no trabajaron en la última semana). Es elevado el porcentaje de personas que dicen estar sobreocupadas (30%). De estos, el 70% son hombres.
df_1['Tamaño_empr'].value_counts()
0) Ns/Nr 3049 1) 1 pers 2661 2) 2 pers 1386 9) 41 a 100 pers 1378 6) 6 a 10 pers 1319 7) 11 a 25 pers 1236 8) 26 a 40 pers 1216 10) 101 a 200 pers 934 12) Más de 500 pers 891 3) 3 pers 878 11) 201 a 500 pers 683 4) 4 pers 548 5) 5 pers 505 Name: Tamaño_empr, dtype: int64
df_1['Tamaño_empr'].value_counts(normalize=True)
0) Ns/Nr 0.182750 1) 1 pers 0.159494 2) 2 pers 0.083074 9) 41 a 100 pers 0.082594 6) 6 a 10 pers 0.079058 7) 11 a 25 pers 0.074083 8) 26 a 40 pers 0.072884 10) 101 a 200 pers 0.055982 12) Más de 500 pers 0.053404 3) 3 pers 0.052625 11) 201 a 500 pers 0.040937 4) 4 pers 0.032846 5) 5 pers 0.030269 Name: Tamaño_empr, dtype: float64
df_1.Sexo.groupby(df_1['Tamaño_empr']).value_counts(normalize=True)
Tamaño_empr Sexo
0) Ns/Nr F 0.598885
M 0.401115
1) 1 pers M 0.593386
F 0.406614
10) 101 a 200 pers M 0.570664
F 0.429336
11) 201 a 500 pers M 0.622255
F 0.377745
12) Más de 500 pers M 0.586981
F 0.413019
2) 2 pers M 0.636364
F 0.363636
3) 3 pers M 0.682232
F 0.317768
4) 4 pers M 0.645985
F 0.354015
5) 5 pers M 0.681188
F 0.318812
6) 6 a 10 pers M 0.576952
F 0.423048
7) 11 a 25 pers M 0.538026
F 0.461974
8) 26 a 40 pers F 0.508224
M 0.491776
9) 41 a 100 pers M 0.539913
F 0.460087
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='Tamaño_empr',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 800,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Tamaño de la empresa',
xaxis_title = 'Recuento de personas',
title_text="TAMAÑO DE LA EMPRESA (Se incluye diferenciación por sexo)")
fig.show()
Sin tener en cuenta la categoría No sabe/No responde, se observa que la mayoría de los encuestados trabajan en empresas o negocios unipersonales (16%). En el otro extremo, solo el 5% trabaja en empresas de más de 500 empleados.
Para todos los casos, hay mayor participación de hombres que mujeres, con excepción de las empresas de 26 a 40 empleados, donde la composición según sexo es prácticamente 50/50.
df_1['Tamaño_empr_2'].value_counts(normalize=True)
Microempresa 0.358307 Pequeña 0.226025 0) Ns/Nr 0.182750 Mediana 0.138576 Grande 0.094342 Name: Tamaño_empr_2, dtype: float64
df_1.Sexo.groupby(df_1['Tamaño_empr_2']).value_counts(normalize=True)
Tamaño_empr_2 Sexo
0) Ns/Nr F 0.598885
M 0.401115
Grande M 0.602287
F 0.397713
Mediana M 0.552336
F 0.447664
Microempresa M 0.628638
F 0.371362
Pequeña M 0.536728
F 0.463272
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='Tamaño_empr_2',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 600,
width = 700,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Tamaño de la empresa 2',
xaxis_title = 'Recuento de personas',
title_text="TAMAÑO DE LA EMPRESA (Se incluye diferenciación por sexo)")
fig.show()
Habiendo hecho una reducción de categorías, ahora vemos que casi el 36% de los trabajadores de la muestra pertenecen a microempresas (recordemos que más arriba las definimos como aquellas con 5 o menos empleados). Luego tenemos que el 23% de los trabajadores se ubican en el rango de las empresas pequeñas (de 6 a 40 empleados). Por su parte, el 14% declara trabajar en empresas medianas (41 a 200 trabajadores) y el 9% en empresas grandes (aquellas con más de 200 trabajadores).
En cuanto a la participación por género, pareciera ser pareja para las pequeñas y medianas empresas, mientras que la brecha se hace más evidente en los extremos (microempresas y grandes empresas). En estas últimas, la composición de varones y mujeres es aproximadamente 60/40.
df_1['CARACTER_OCUP'].value_counts(normalize=True)
30 0.114541 72 0.103033 10 0.101594 80 0.070367 41 0.068149 56 0.054184 55 0.051846 34 0.045613 40 0.044953 57 0.036262 20 0.035663 53 0.035603 82 0.028111 5 0.023615 48 0.017562 58 0.016003 47 0.014984 32 0.012827 36 0.012467 11 0.009590 92 0.008032 35 0.007792 6 0.007372 31 0.006713 33 0.005934 81 0.005634 3 0.005574 60 0.004735 51 0.004675 46 0.004615 45 0.004435 99 0.004256 42 0.003836 90 0.003836 50 0.003476 44 0.003177 70 0.003117 49 0.003057 43 0.002338 7 0.002038 54 0.001558 61 0.001379 71 0.001079 52 0.000959 62 0.000719 64 0.000719 91 0.000659 2 0.000539 63 0.000360 1 0.000180 4 0.000180 0 0.000060 Name: CARACTER_OCUP, dtype: float64
fig= px.histogram(df_1,
y='CARACTER_OCUP',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 1300,
width = 900,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Ocupación',
xaxis_title = 'Recuento de personas',
title_text="Ocupaciones de los trabajadores (Se incluye diferenciación por sexo)")
fig.show()
Vemos que las tres ramas de ocupaciones más populares en la muestra son "30- Comercialización directa" (con un 11% de participación), "72- Construcción edilicia, de obras de infraestructura y de redes de distribución de energía, agua potable, gas, telefonía y petróleo" y "10- Gestión administrativa, planificación y control de gestión" (ambas con un porcentaje del 10% respecto del total de los trabajadores).
En cuanto a la distribución según sexo, para la primera y tercera categoría, hay mayor participación por parte de las mujeres (53% y 56% respectivamente), mientras que para la segunda, relativa a la construcción edilicia e infraestructuras de servicios, el grado de participación del género femenino es muy bajo (alrededor del 3%).
df_1['JERARQUIA_OCUP'].value_counts(normalize=True)
Asalariados 0.731719 Cuenta propia 0.202230 Direccion 0.039499 Jefes 0.022237 Ns/Nr 0.004256 0 0.000060 Name: JERARQUIA_OCUP, dtype: float64
#Reemplazamos el valor cero por Ns/Nr
df_1['JERARQUIA_OCUP']=df_1['JERARQUIA_OCUP'].replace(0,'Ns/Nr')
df_1.Sexo.groupby(df_1['JERARQUIA_OCUP']).value_counts(normalize=True)
JERARQUIA_OCUP Sexo
Asalariados M 0.531455
F 0.468545
Cuenta propia M 0.593954
F 0.406046
Direccion M 0.676783
F 0.323217
Jefes M 0.649596
F 0.350404
Ns/Nr M 0.708333
F 0.291667
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='JERARQUIA_OCUP',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 500,
width = 900,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Jerarquía ocupacional',
xaxis_title = 'Recuento de personas',
title_text="Jerarquía ocupacional (Se incluye diferenciación por sexo)")
fig.show()
El 73% de los trabajadores presenta la jerarquía más baja, como asalariados. Por su parte, entre los jefes y directivos suman el 6% del total del dataset. Notar que para estos últimos, la participación del sexo masculino supera el 65%.
df_1['TECNOLOGIA_OCUP'].value_counts(normalize=True)
Sin op. máq. 0.582055 Op. sist. informatizados 0.264265 Con op. máq. 0.087629 Ns/Nr 0.065991 0 0.000060 Name: TECNOLOGIA_OCUP, dtype: float64
#Reemplazamos el valor cero por Ns/Nr
df_1['TECNOLOGIA_OCUP']=df_1['TECNOLOGIA_OCUP'].replace(0,'Ns/Nr')
df_1.Sexo.groupby(df_1['TECNOLOGIA_OCUP']).value_counts(normalize=True)
TECNOLOGIA_OCUP Sexo
Con op. máq. M 0.844049
F 0.155951
Ns/Nr M 0.669691
F 0.330309
Op. sist. informatizados F 0.548197
M 0.451803
Sin op. máq. M 0.542272
F 0.457728
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='TECNOLOGIA_OCUP',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 500,
width = 900,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Tecnología ocupacional',
xaxis_title = 'Recuento de personas',
title_text="Tipo de tecnología empleada por los trabajadores (Se incluye diferenciación por sexo)")
fig.show()
El 68% de las personas trabajan sin operar ningún tipo de máquina, de los cuales el 54% son hombres.
Quienes operan algún tipo de máquina representan solo el 9% de los trabajadores, siendo que el 84% de ellos son varones.
Las personas que trabajan con computadoras representan el 26% de la muestra. Aquí la proporción según sexo se invierte, siendo que las mujeres tienen una participación del 54% para esta categoría.
df_1['CALIFICACION_OCUP'].value_counts(normalize=True)
Operativo 0.528710 No calificado 0.209902 Técnicos 0.180652 Profesionales 0.076241 Ns/Nr 0.004495 Name: CALIFICACION_OCUP, dtype: float64
df_1.Sexo.groupby(df_1['CALIFICACION_OCUP']).value_counts(normalize=True)
CALIFICACION_OCUP Sexo
No calificado F 0.583952
M 0.416048
Ns/Nr M 0.706667
F 0.293333
Operativo M 0.640970
F 0.359030
Profesionales M 0.505503
F 0.494497
Técnicos F 0.527870
M 0.472130
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='CALIFICACION_OCUP',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 500,
width = 900,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Calificación ocupacional',
xaxis_title = 'Recuento de personas',
title_text="Calificación de los trabajadores (Se incluye diferenciación por sexo)")
fig.show()
Entre el personal operativo y no calificado se incluyen el 73% de los trabajadores (esto es coherente con lo analizado más arriba en la variable Jerarquía ocupacional).
Por su parte, los técnicos representan el 18% de la muestra, mientras que los profesionales menos del 8%. Para ambas categorías, la distribución según sexo es equitativa.
df_1['ACTIV_ECON'].value_counts(normalize=True)
48 0.153800
84 0.134980
40 0.100396
85 0.088947
97 0.072165
...
63 0.000300
50 0.000240
39 0.000060
12 0.000060
37 0.000060
Name: ACTIV_ECON, Length: 77, dtype: float64
fig= px.histogram(df_1,
y='ACTIV_ECON',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 1400,
width = 1000,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Código de actividad económica',
xaxis_title = 'Recuento de personas',
title_text="Actividad económica del lugar de trabajo (Se incluye diferenciación por sexo)")
fig.show()
Analizando las tres primeras categorías, se observa que el 15% de las personas trabajan en empreas dedicadas a "48- Comercio, Excepto de Vehículos Automotores y Motocicletas". La segunda categoría en orden de importancia, con un porcentaje del 13%, es "84- Administración Pública y Defensa; Planes de Seguro Social Obligatorio". La tercer actividad económica es "40- Construcción", con un 10% de participación.
Notar que esta variable guarda relación con la variable 'Carácter ocupacional'. En ambas, las tres primeras categorías son muy similares.
Esta variable se creó a fin de agrupar las actividades económicas en categorías más generales, para simplificar el análisis.
df_1['CAT_ECON'].value_counts(normalize=True)
G 0.181012 O 0.134980 C 0.104531 F 0.100396 P 0.088947 T 0.072165 Q 0.065332 H 0.043695 S 0.041117 N 0.039139 I 0.029070 M 0.027811 R 0.017801 J 0.016603 K 0.013965 W 0.008691 E 0.006293 L 0.004076 D 0.003896 A 0.000480 Name: CAT_ECON, dtype: float64
df_1.Sexo.groupby(df_1['CAT_ECON']).value_counts(normalize=True)
CAT_ECON Sexo
A M 0.875000
F 0.125000
C M 0.692661
F 0.307339
D M 0.876923
F 0.123077
E M 0.723810
F 0.276190
F M 0.968955
F 0.031045
G M 0.585762
F 0.414238
H M 0.877915
F 0.122085
I M 0.511340
F 0.488660
J M 0.689531
F 0.310469
K F 0.506438
M 0.493562
L M 0.514706
F 0.485294
M F 0.500000
M 0.500000
N M 0.693721
F 0.306279
O M 0.550178
F 0.449822
P F 0.725741
M 0.274259
Q F 0.742202
M 0.257798
R M 0.659933
F 0.340067
S F 0.530612
M 0.469388
T F 0.973422
M 0.026578
W M 0.682759
F 0.317241
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='CAT_ECON',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 900,
width = 800,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Código de categoría económica',
xaxis_title = 'Recuento de personas',
title_text="Categoría económica del lugar de trabajo (Se incluye diferenciación por sexo)")
fig.show()
Continuando con el criterio de analizar las tres primeras categorías, se observa que el 18% de las personas trabajan en empreas dedicadas a "G- Comercio al por Mayor y al por Menor; Reparación de Vehículos, Automotores y Motocicletas". La segunda categoría en orden de importancia, con un porcentaje del 13%, es "O- Administración Pública y Defensa; Planes de Seguro Social Obligatorio". Estas dos primeras categorías coinciden con la variable anterior.
Sin embargo, en este caso, el tercer lugar está prácticamente compartido por los grupos "C- Industria Manufacturera" y "F- Construcción", ambos con un 10% de participación. Este cambio se debe a que la categoría Industria Manufacturera abarca 24 actividades económicas, que al sumarlas cobran mayor relevancia.
En cuanto a las diferencias por género, por un lado, la categoría con menor participación de mujeres es "F- Construcción", siendo ésta igual a 3%. Por el otro lado, la categoría con menor participación de hombres es "T- Actividades de los Hogares como Empleadores de Personal Doméstico; Actividades de los Hogares como Productores de Bienes o Servicios para Uso Propio", siendo ésta inferior a 3%.
df_1['Cant_Ocup'].value_counts(normalize=True)
1 0.914469 2 0.074682 3 0.007252 4 0.001798 5 0.001259 6 0.000360 7 0.000180 Name: Cant_Ocup, dtype: float64
#Convertimos la columna 'Cant_Ocup'a tipo entero.
df_1['Cant_Ocup']=df_1['Cant_Ocup'].astype(int)
#Convertimos la columna 'Cant_Ocup'a tipo texto.
df_1['Cant_Ocup']=df_1['Cant_Ocup'].astype(str)
df_1.Sexo.groupby(df_1['Cant_Ocup']).value_counts(normalize=True)
Cant_Ocup Sexo
1 M 0.564331
F 0.435669
2 F 0.544141
M 0.455859
3 F 0.661157
M 0.338843
4 F 0.766667
M 0.233333
5 F 0.857143
M 0.142857
6 F 0.833333
M 0.166667
7 F 1.000000
Name: Sexo, dtype: float64
fig= px.histogram(df_1,
y='Cant_Ocup',
text_auto='.2s',
color= 'Sexo')
fig.update_layout(
height = 500,
width = 800,
yaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Cantidad de ocupaciones',
xaxis_title = 'Recuento de personas',
title_text="Cantidad de ocupaciones (Se incluye diferenciación por sexo)")
fig.show()
Vemos que el 91% de las personas tienen una sola ocupación, mientras que del resto, el 7% afirman tener dos ocupaciones y solo menos del 1% declara tener 3 ocupaciones.
df_1['Edad_2'].value_counts(normalize=True)
(38.0, 46.0] 0.216795 (30.0, 38.0] 0.214097 (46.0, 55.0] 0.184968 (24.0, 30.0] 0.157336 (55.0, 65.0] 0.126468 (17.999, 24.0] 0.100336 Name: Edad_2, dtype: float64
df_1.Sexo.groupby(df_1['Edad_2']).value_counts(normalize=True)
Edad_2 Sexo
(17.999, 24.0] M 0.614098
F 0.385902
(24.0, 30.0] M 0.564190
F 0.435810
(30.0, 38.0] M 0.550952
F 0.449048
(38.0, 46.0] M 0.539674
F 0.460326
(46.0, 55.0] M 0.512962
F 0.487038
(55.0, 65.0] M 0.577251
F 0.422749
Name: Sexo, dtype: float64
fig, axes = plt.subplots(figsize=(9,6))
#grafico de barras
sns.countplot(x=df_1['Edad_2'], color='#08519C')
axes.set_title('Recuento de trabajadores según edad')
plt.show()
Al dividir la variable edad en 6 rangos y transformarla en categórica, los rangos que mayor cantidad de trabajadores tienen son 30-38 años y 38-46 años, cada uno representando el 21% de la muestra. La participación de mujeres en estos intervalos ronda el 45%.
Recordemos que previamente, para el EDA, se seleccionaron solo aquellas personas de entre 18 y 65 años de edad.
Es una de las formas más simples de análisis estadístico, que se utiliza para averiguar si existe una relación entre dos conjuntos de valores. Por lo general involucra las variables X e Y.
El procedimiento consistirá en ir comparando todas las variables de interés con la variable objetivo "Ingresos", a fin de indagar sobre la veracidad de las hipótesis planteadas e identificar patrones que luego serán tenidos en cuenta en la construcción del modelo predictivo.
fig = make_subplots(rows=1, cols=2,
subplot_titles=('Histograma', "Boxplot"))
fig.add_trace(
go.Histogram( x=df_1['CAT_OCUP'],
y=df_1['Ingresos'],
histfunc='avg'
),
row=1, col=1
)
fig.add_trace(
go.Box( x=df_1['CAT_OCUP'],
y=df_1['Ingresos'],
boxmean='sd', # represententa el promedio y la desviación estándar
boxpoints='suspectedoutliers', # sólo outliers sospechosos
marker=dict(
color='rgba(219, 64, 82, 0.6)',
outliercolor='rgb(8,81,156)',
line=dict(
outliercolor='rgb(8,81,156)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
),
row=1, col=2
)
fig.update_layout(height=600, width=1200, title_text="CATEGORÍA OCUPACIONAL VS. INGRESOS",
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None, showlegend=False,
xaxis = {'categoryorder':'total ascending'})
fig.show()
Recordemos cuál es la Hipótesis 1 planteada:
A partir de histograma, podemos ver que en promedio, los trabajadores en relación de dependencia (obreros o empleados) ganan unos $18.000 más que los cuentapropistas, es decir, aproximadamente un 43% más. Sin embargo la máxima brecha se da entre patrones y cuentapropistas, siendo que los primeros ganan más del doble que los segundos.
Además, si consideramos tanto a los patrones como a los cuentapropistas dentro del grupo de independientes, según los boxplots, existe mayor variabilidad en los ingresos de los trabajadores independientes, dados los valores de las desviaciones estándar para cada grupo, por lo que la hipótesis 1 planteada al inicio podría ser cierta.
fig= px.histogram(df_1,
x='Sexo',
y='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=px.colors.sequential.RdBu
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 600,
width = 400,
title={
'text': 'INGRESOS SEGÚN SEXO',
},
xaxis = {'categoryorder':'category ascending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Sin ahondar en otras variables, vemos que existe una profunda brecha salarial de género. Los datos indican que, en promedio, los hombres ganan un 29% más que las mujeres.
fig= px.histogram(df_1,
x='CAT_OCUP',
y='Ingresos',
hover_name='Ingresos',
color= 'Sexo',
barmode='group',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=px.colors.sequential.RdBu
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 700,
width = 600,
title={
'text': 'INGRESOS SEGÚN SEXO Y CATEGORÍA OCUPACIONAL',
},
xaxis = {'categoryorder':'total ascending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Recordemos cuál es la Hipótesis 2 planteada:
Para los patrones, que son quienes perciben los ingresos más altos, la brecha de género es del 15,8% a favor de los varones.
En el caso de los trabajadores asalariados (obreros o empleados) la diferencia alcanza el valor de 29,6%.
Para la categoría de cuentapropistas, la brecha se profundiza aún más, ganando los hombres un 34,8% más que las mujeres.
Por lo tanto, la Hipótesis 2 pareciera ser completamente cierta no solo para la categoría de asalariados sino también para los trabajadores independientes.
A continuación iremos contrastando el resto de las variables con la variable ingresos, y, en algunos casos, ampliaremos cada gráfico por categoría ocupacional y por sexo.
fig= px.histogram(df_1,
x='REGION',
y='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#22516D']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 700,
title={
'text': 'LUGAR DE RESIDENCIA VS. INGRESOS',
},
xaxis = {'categoryorder':'total descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = 'Región del país',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
fig= px.box(df_1,
x='REGION',
y='Ingresos',
hover_name='Ingresos',
color='REGION',
color_discrete_sequence=['#8E8DBE','#7A306C','#96F550','#845F95','#A9E4EF','#81F495']
)
fig.update_xaxes(categoryorder='array', categoryarray= ['PATAGONIA','GBA','PAMPEANA','CUYO', 'NEA','NOA'])
fig.update_layout(
height = 500,
width = 700,
title={
'text': 'LUGAR DE RESIDENCIA VS. INGRESOS',
},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = 'Región del país',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Recordemos cuál es la Hipótesis 3 planteada:
De los gráficos obtenemos los siguientes insights:
• Existe una marcada diferencia entre los ingresos percibidos según la región del país en que residen los trabajadores.
• Una persona que vive en la región patagónica gana, en promedio, un 85% más que alguien que trabaja en el noroeste argentino.
• No se verifica la hipótesis 3, en la que se supuso que aquellos trabajadores que residen en la capital del país y alrededores ganan más dinero que el resto de las regiones. Las personas que más ingresos perciben son, en promedio, quienes viven en la Patagonia, superando en un 14% a los ingresos de la región del Gran Buenos Aires.
fig= px.histogram(df_1,
y='REGION',
x='Ingresos',
hover_name='Ingresos',
color= 'CAT_OCUP',
barmode='group',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#8E8DBE', '#475669','#7A306C']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 900,
title={
'text': 'LUGAR DE RESIDENCIA VS. INGRESOS- Según categoría ocupacional',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Región del país',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Comparando trabajadores asalariados versus cuentapropistas, la brecha máxima se da en la región de Cuyo, donde un obrero o empleado gana un 47% más que alguien que trabaja de forma independiente y que no es patrón.
fig= px.histogram(df_1,
x='REGION',
y='Ingresos',
hover_name='Ingresos',
color= 'Sexo',
barmode='group',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#845F95', '#81F495']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 700,
title={
'text': 'LUGAR DE RESIDENCIA VS. INGRESOS- Según sexo',
},
xaxis = {'categoryorder':'total descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = 'Región del país',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
La región más desigual en cuanto a género es Gran Buenos Aires, para la cual la brecha salarial promedio entre varones y mujeres es del 34%, a favor de los primeros.
A continuación, analizaremos cuáles son los aglomerados urbanos con más y menos ingresos.
fig= px.histogram(df_1,
y='AGLOMERADO',
x='Ingresos',
color='MAS_500',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s'
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 700,
width = 600,
title={
'text': 'AGLOMERADO URBANO VS. INGRESOS (Color según tamaño)',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de aglomerado',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Los tres aglomerados urbanos con mayores ingresos son:
Ambos pertenecen a la región patagónica. En tamaño, están clasificados como menor a 500 mil habitantes.
Los tres aglomerados urbanos con menores ingresos son:
Todos ellos son aglomerados con menos de 500 mil personas y presentan un ingreso promedio de 38 mil pesos.
Se podría suponer, entonces, que alguien que reside en alguna de estas ciudades de la región patagónica, al sur del país, gana en promedio 2,5 más que quien reside en los tres últimos aglomerados mencionados, situados más al norte del país.
fig= px.histogram(df_1,
y='AGLOMERADO',
x='Ingresos',
color='REGION',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 700,
width = 600,
title={
'text': 'AGLOMERADO URBANO VS. INGRESOS (Color según Región)',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de aglomerado',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Al distinguir los aglomerados por color según su región de pertenencia, es posible observar las desigualdades que se dan dentro de una misma región.
Por ejemplo, la diferencia que se da en la región del Gran Buenos Aires, entre CABA y el resto de los Partidos del GBA. Situación similar se da en Cuyo, donde los habitantes de "26- Gran San Luis", perciben ingresos considerablemente mayores a los de "10- Gran Mendoza" o "27- Gran San Juan".
Realizaremos el gráfico para la primer variable de nivel educativo: 'NIVEL_ED'.
fig= px.box(df_1,
x='NIVEL_ED',
y='Ingresos',
hover_name='Ingresos',
color_discrete_sequence=['#3A182E']
)
fig.update_layout(
height = 500,
width = 800,
title={
'text': 'NIVEL EDUCATIVO VS. INGRESOS',
},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Ahora realizaremos el gráfico para la segunda variable de nivel educativo: 'NIVEL_ED_2'.
fig= px.box(df_1,
x='NIVEL_ED_2',
y='Ingresos',
hover_name='Ingresos',
color_discrete_sequence=['#3A182E']
)
fig.update_layout(
height = 500,
width = 800,
title={
'text': 'NIVEL EDUCATIVO (Variable NIVEL_ED_2) VS. INGRESOS',
},
xaxis = {'categoryorder':'category descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Recordemos cuál es la Hipótesis 4 planteada:
Analizando ambos gráficos, la hipótesis 4 pareciera ser cierta, es decir, a mayor nivel educativo alcanzado, mayores son los ingresos del trabajador. Esto se pone especialmente en evidencia cuando incorporamos la categoría de posgrado. Quien tiene estudios de posgrado gana, en promedio, casi 4 veces más que quien solo asistió al nivel inicial (jardín) o que no tiene instrucción.
fig= px.histogram(df_1,
y='NIVEL_ED_2',
x='Ingresos',
hover_name='Ingresos',
color= 'CAT_OCUP',
barmode='group',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#D6A278', '#8E8DBE','#3A182E']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 800,
width = 1000,
title={
'text': 'NIVEL EDUCATIVO VS. INGRESOS- Según categoría ocupacional',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
En primer lugar, se observa que quienes mayores ingresos perciben son patrones o empleados con nivel de posgrado.
Las máximas brechas entre categorías ocupacionales se dan para el nivel terciario, donde un patrón gana 2,2 veces más que un cuentapropista, mientras que un empleado gana un 55% más que quien trabaja por su cuenta.
fig= px.histogram(df_1,
x='NIVEL_ED_2',
y='Ingresos',
hover_name='Ingresos',
color= 'Sexo',
barmode='group',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#8E8DBE','#3A182E']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 900,
title={
'text': 'NIVEL EDUCATIVO VS. INGRESOS- Según sexo',
},
xaxis = {'categoryorder':'total descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Sin tener en cuenta la categoría de educación especial, la máxima brecha salarial de género se da para quienes tienen estudios de nivel primario, siendo ésta igual a 80%. De todos modos, es llamativo el hecho de que la brecha prácticamente no se reduce a mayor nivel educativo, siendo que, para el nivel de posgrado, los hombres ganan en promedio un 65% más que las mujeres.
Bloque con sangría
plt.figure(figsize=(12,6))
sns.lineplot(data=df_1, x='Edad', y= 'Ingresos', color='#542344')
plt.title("Ingresos vs. Edad", fontsize=15, verticalalignment='bottom');
plt.xlabel("Edad");
plt.ylabel("Ingresos en pesos argentinos")
Text(0, 0.5, 'Ingresos en pesos argentinos')
Recordemos cuál es la Hipótesis 5 planteada:
Se observa en la curva de ingresos vs. edad, que los ingresos van en aumento hasta aproximadamente los 55 años, valor a partir del cual comienzan a descender. Por lo tanto, la hipótesis 5 pareciera ser cierta solo hasta esa edad, y no para los últimos 10 años de vida laboral.
plt.figure(figsize=(10,8))
sns.lineplot(data=df_1, x='Edad', y= 'Ingresos', hue='CAT_OCUP', palette= 'rocket')
plt.title("Ingresos Trabajadores vs. Edad- Según categoría ocupacional", fontsize=15, verticalalignment='bottom');
plt.xlabel("Edad");
plt.ylabel("Ingresos en pesos argentinos")
Text(0, 0.5, 'Ingresos en pesos argentinos')
Vemos que existe una brecha de ingresos entre asalariados y cuentapropistas que se mantiene en el transcurso de la vida de las personas. Ampliaremos sobre esto en el análisis de la variable edad discretizada en rangos.
Además, la curva de ingresos de los patrones es más variable y presenta un aspecto cíclico, por momentos intersectando en sus picos inferiores a la curva de los asalariados.
plt.figure(figsize=(10,8))
sns.lineplot(data=df_1, x='Edad', y= 'Ingresos', hue='Sexo', palette= ['#843B62','#201E50'])
plt.title("Ingresos Trabajadores vs. Edad- Según sexo", fontsize=15, verticalalignment='bottom');
plt.xlabel("Edad");
plt.ylabel("Ingresos en pesos argentinos")
Text(0, 0.5, 'Ingresos en pesos argentinos')
Vemos que existe una brecha salarial de género que se mantiene en el transcurso de la vida de las personas. Ampliaremos sobre esto en el análisis de la variable edad discretizada en rangos.
plt.figure(figsize=(8,6))
sns.barplot(y='Ingresos',x='Edad_2',data=df_1, color='#542344')
plt.title("INGRESOS VS. EDAD", color='black')
plt.xlabel("Rango etáreo",color='black')
plt.ylabel("Ingresos en pesos argentinos",color='black')
Text(0, 0.5, 'Ingresos en pesos argentinos')
df_1.Ingresos.groupby(df_1['Edad_2']).mean()
Edad_2 (17.999, 24.0] 31809.354839 (24.0, 30.0] 47213.657524 (30.0, 38.0] 56590.521837 (38.0, 46.0] 60406.821122 (46.0, 55.0] 65277.970188 (55.0, 65.0] 60541.764929 Name: Ingresos, dtype: float64
Se observa que los ingresos máximos se dan para la franja etárea de 46 a 55 años, duplicando en promedio a los ingresos de la franja 18 a 24 años.
df_1.groupby(['Edad_2','CAT_OCUP']).agg({'Ingresos':['mean','median']}).reset_index()
| Edad_2 | CAT_OCUP | Ingresos | ||
|---|---|---|---|---|
| mean | median | |||
| 0 | (17.999, 24.0] | Cuenta Propia | 23282.183908 | 20000.0 |
| 1 | (17.999, 24.0] | Obrero o empleado | 33370.864286 | 30000.0 |
| 2 | (17.999, 24.0] | Patron | 34846.153846 | 30000.0 |
| 3 | (24.0, 30.0] | Cuenta Propia | 33440.326340 | 30000.0 |
| 4 | (24.0, 30.0] | Obrero o empleado | 49203.109513 | 40000.0 |
| 5 | (24.0, 30.0] | Patron | 86762.195122 | 50000.0 |
| 6 | (30.0, 38.0] | Cuenta Propia | 42608.691843 | 30000.0 |
| 7 | (30.0, 38.0] | Obrero o empleado | 59377.663949 | 50000.0 |
| 8 | (30.0, 38.0] | Patron | 72247.191011 | 60000.0 |
| 9 | (38.0, 46.0] | Cuenta Propia | 44866.800000 | 35000.0 |
| 10 | (38.0, 46.0] | Obrero o empleado | 63865.078267 | 58000.0 |
| 11 | (38.0, 46.0] | Patron | 83152.380952 | 60000.0 |
| 12 | (46.0, 55.0] | Cuenta Propia | 45650.911854 | 35500.0 |
| 13 | (46.0, 55.0] | Obrero o empleado | 69558.817708 | 58500.0 |
| 14 | (46.0, 55.0] | Patron | 89887.096774 | 70000.0 |
| 15 | (55.0, 65.0] | Cuenta Propia | 41439.669421 | 30000.0 |
| 16 | (55.0, 65.0] | Obrero o empleado | 66453.146515 | 60000.0 |
| 17 | (55.0, 65.0] | Patron | 93323.232323 | 70000.0 |
plt.figure(figsize=(12,7))
sns.barplot(y='Ingresos',x='Edad_2',data=df_1, hue='CAT_OCUP', palette= 'rocket')
plt.title("INGRESOS VS. EDAD- Según categoría ocupacional", color='black')
plt.xlabel("Rango etáreo",color='black')
plt.ylabel("Ingresos en pesos argentinos",color='black')
Text(0, 0.5, 'Ingresos en pesos argentinos')
Teniendo en cuenta que la edad jubilatoria en Argentina es 60 y 65 años para mujeres y hombres respectivamente, los trabajadores asalariados ganan en promedio más dinero que los cuentapropistas a lo largo de toda la vida laboral activa. Esta brecha se hace más notoria en el rango etáreo de 55 a 65 años, alcanzando un valor de 60%.
Por su parte, los patrones son quienes ganan más dinero a lo largo de todas las edades.
plt.figure(figsize=(8,6))
sns.barplot(y='Ingresos',x='Edad_2',data=df_1, hue='Sexo', palette= ['#843B62','#201E50'])
plt.title("INGRESOS VS. EDAD- Según sexo", color='black')
plt.xlabel("Rango etáreo",color='black')
plt.ylabel("Ingresos en pesos argentinos",color='black')
Text(0, 0.5, 'Ingresos en pesos argentinos')
df_1.groupby(['Edad_2','Sexo']).agg({'Ingresos':['mean','median']}).reset_index()
| Edad_2 | Sexo | Ingresos | ||
|---|---|---|---|---|
| mean | median | |||
| 0 | (17.999, 24.0] | F | 27738.049536 | 22000.0 |
| 1 | (17.999, 24.0] | M | 34367.782101 | 30000.0 |
| 2 | (24.0, 30.0] | F | 42710.140734 | 35000.0 |
| 3 | (24.0, 30.0] | M | 50692.403781 | 43200.0 |
| 4 | (30.0, 38.0] | F | 47978.175810 | 40000.0 |
| 5 | (30.0, 38.0] | M | 63609.933943 | 52000.0 |
| 6 | (38.0, 46.0] | F | 51525.339339 | 45000.0 |
| 7 | (38.0, 46.0] | M | 67982.470287 | 60000.0 |
| 8 | (46.0, 55.0] | F | 54901.687957 | 45000.0 |
| 9 | (46.0, 55.0] | M | 75129.866709 | 60000.0 |
| 10 | (55.0, 65.0] | F | 49429.791480 | 40000.0 |
| 11 | (55.0, 65.0] | M | 68679.597701 | 60000.0 |
En cuanto a género, se evidencia que los hombres perciben más ingresos que las mujeres a lo largo de toda la vida laboral activa. La brecha máxima ocurre en el rango etáreo de 55 a 65 años, es decir, al acercarse los trabajadores a la edad jubilatoria, siendo ésta igual al 39%.
plt.figure(figsize=(12,6))
sns.scatterplot(data=df_1, x='Horas_sem', y= 'Ingresos', color= '#B2BCD1')
sns.lineplot(data=df_1, x='Horas_sem', y= 'Ingresos', color= '#542344')
plt.title("Ingresos vs Horas semanales trabajadas", fontsize=15, verticalalignment='bottom');
plt.xlabel("Horas semanales");
plt.ylabel("Ingresos en millones de pesos argentinos")
Text(0, 0.5, 'Ingresos en millones de pesos argentinos')
#Mismo gráfico que el anterior pero en plotly, para poder hacer zoom e indagar sobre la pendiente de la líena de tendencia.
fig = px.scatter(df_1,
x="Horas_sem",
y="Ingresos",
trendline="lowess",
trendline_color_override= 'black',
title= 'Ingresos según horas semanales trabajadas'
)
fig.update_layout(
height = 500,
width = 900,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
yaxis_title = 'Ingresos en millones de pesos argentinos'
)
fig.update_traces(marker=dict(size=3,
color='#8CACAC'))
fig.show()
fig = px.scatter(df_1,
x="Horas_sem",
y="Ingresos",
facet_col="Sexo",
color='Sexo',
trendline="lowess",
trendline_color_override= 'black',
title= 'Ingresos según horas semanales trabajadas según sexo'
)
fig.update_layout(
height = 400,
width = 1150,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
yaxis_title = 'Ingresos en millones de pesos argentinos'
)
fig.update_traces(marker=dict(size=4,
))
fig.show()
fig = px.scatter(df_1,
x="Horas_sem",
y="Ingresos",
facet_col="CAT_OCUP",
color='CAT_OCUP',
trendline="lowess",
trendline_color_override= 'black',
title= 'Ingresos según horas semanales trabajadas según categoría ocupacional'
)
fig.update_layout(
height = 400,
width = 1150,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
yaxis_title = 'Ingresos en millones de pesos argentinos'
)
fig.update_traces(marker=dict(size=4,
color='#7C98B3'))
fig.show()
Recordemos cuál es la Hipótesis 6 planteada:
Al hacer zoom en los gráficos, las curvas de ingresos vs. horas semanales trabajadas presentan las siguientes características:
Como conclusión, no está tan claro cómo influirá esta variable a la hora de predecir los ingresos. Se definirá en la etapa de modelado.
fig = make_subplots(rows=1, cols=2,
subplot_titles=('Histograma', "Boxplot"))
fig.add_trace(
go.Histogram( x=df_1['INTENSI'],
y=df_1['Ingresos'],
histfunc='avg'
),
row=1, col=1
)
fig.add_trace(
go.Box( x=df_1['INTENSI'],
y=df_1['Ingresos'],
boxmean='sd', # represententa el promedio y la desviación estándar
boxpoints='suspectedoutliers', # sólo outliers sospechosos
marker=dict(
color='rgba(219, 64, 82, 0.6)',
outliercolor='rgb(8,81,156)',
line=dict(
outliercolor='rgb(8,81,156)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
),
row=1, col=2
)
fig.update_layout(height=600, width=1200, title_text="INTENSIDAD DE LA OCUPACIÓN VS. INGRESOS",
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None, showlegend=False,
xaxis = {'categoryorder':'total descending'})
fig.show()
Cantidad de ocupaciones:
fig = make_subplots(rows=1, cols=2,
subplot_titles=('Histograma', "Boxplot"))
fig.add_trace(
go.Histogram( x=df_1['Cant_Ocup'],
y=df_1['Ingresos'],
histfunc='avg'
),
row=1, col=1
)
fig.add_trace(
go.Box( x=df_1['Cant_Ocup'],
y=df_1['Ingresos'],
boxmean='sd', # represententa el promedio y la desviación estándar
boxpoints='suspectedoutliers', # sólo outliers sospechosos
marker=dict(
color='rgba(219, 64, 82, 0.6)',
outliercolor='rgb(8,81,156)',
line=dict(
outliercolor='rgb(8,81,156)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
),
row=1, col=2
)
fig.update_layout(height=600, width=1200, title_text="CANTIDAD DE OCUPACIONES VS. INGRESOS",
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None, showlegend=False,
xaxis = {'categoryorder':'total descending'})
fig.show()
Recordemos cuál es la Hipótesis 7 planteada:
Esta hipótesis parece verificarse, como es de suponer, si analizamos la diferencia entre ingresos percibidos por subocupados y ocupados pleno, siendo que los segundos ganan casi 2,4 veces más que los primeros. Sin embargo, la brecha se achica entre sobreocupados y ocupados pleno: la diferencia es igual a 15% a favor de los sobreocupados.
Al analizar la variable Cantidad de ocupaciones, vemos que a mayor cantidad de ocupaciones, menor es el ingreso promedio de los trabajadores. Asimismo, para el conjunto de personas con una sola ocupación, hay mayor dispersión en los valores de ingresos.
fig= px.histogram(df_1,
x='CALIFICACION_OCUP',
y='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#3A182E']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 600,
title={
'text': 'CALIFICACIÓN OCUPACIONAL VS. INGRESOS',
},
xaxis = {'categoryorder':'total descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
fig= px.histogram(df_1,
x='TECNOLOGIA_OCUP',
y='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
color_discrete_sequence=['#1e0454']
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 600,
title={
'text': 'TECNOLOGÍA OCUPACIONAL VS. INGRESOS',
},
xaxis = {'categoryorder':'total descending'},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Recordemos cuál es la Hipótesis 8 planteada:
Sin tener en cuenta las categorías No sabe/No responde, podemos suponer que la hipótesis 8 es verdadera, es decir cuanto más calificada es la persona y más compleja la tecnología que emplea, mayores serán sus ingresos.
Por ejemplo, vemos que en promedio los profesionales ganan 62% más que los técnicos, y que quienes trabajan con computadoras ganan 76% más que quienes no emplean ningún tipo de máquina para trabajar.
Para profundizar el análisis, veremos cuál/cuáles son las ocupaciones con mayores ingresos, graficando la variable Carácter Ocupacional vs. Ingresos. También analizaremos la Jerarquía Ocupacional vs. Ingresos.
fig= px.histogram(df_1,
y='CARACTER_OCUP',
x='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s'
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 1000,
width = 1000,
title={
'text': 'CARACTER OCUPACIONAL VS. INGRESOS',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de carácter ocupacional',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Las tres categorías con mayores ingresos promedio son:
"02- Funcionarios del poder judicial, federal, nacional, provincial, municipal y/o departamental". Con un ingreso promedio de casi 300 mil pesos, supera en un 59% a la categoría ubicada en segundo lugar.
"64- Ocupaciones de la producción pesquera". Con un ingreso promedio de casi $190 mil, supera solo en un 8% a la categoría ubicada en tercer lugar.
"07- Directivos de grandes empresas privadas productoras de bienes y servicios". Ingreso promedio de 173 mil pesos.
Las tres categorías con menos ingresos son:
Comparando los extremos, un funcionario del poder judicial gana en promedio 16 veces más que una empleada doméstica.
fig = make_subplots(rows=1, cols=2,
subplot_titles=('Histograma', "Boxplot"))
fig.add_trace(
go.Histogram( x=df_1['JERARQUIA_OCUP'],
y=df_1['Ingresos'],
histfunc='avg'
),
row=1, col=1
)
fig.add_trace(
go.Box( x=df_1['JERARQUIA_OCUP'],
y=df_1['Ingresos'],
boxmean='sd', # represententa el promedio y la desviación estándar
boxpoints='suspectedoutliers', # sólo outliers sospechosos
marker=dict(
color='rgba(219, 64, 82, 0.6)',
outliercolor='rgb(8,81,156)',
line=dict(
outliercolor='rgb(8,81,156)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
),
row=1, col=2
)
fig.update_layout(height=600, width=1200, title_text="JERARQUÍA OCUPACIONAL VS. INGRESOS",
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None, showlegend=False,
xaxis = {'categoryorder':'total descending'})
fig.show()
Esta variable es muy similar a la de Categoría Ocupacional. Los valores de ingresos medios dan muy similares a ésta, para las categorías de Asalariados y Cuenta propia.
Sin embargo, al comparar la categoría de Patrones con la de Jefes y Dirección, el ingreso promedio de los patrones es menor (alrededor de 83 mil pesos, contra el de Jefes y Directores, cuyo ingreso promedio es cercano a los 100k).
fig = make_subplots(rows=1, cols=2,
subplot_titles=('Histograma', "Boxplot"))
fig.add_trace(
go.Histogram( x=df_1['Tipo_empr'],
y=df_1['Ingresos'],
histfunc='avg'
),
row=1, col=1
)
fig.add_trace(
go.Box( x=df_1['Tipo_empr'],
y=df_1['Ingresos'],
boxmean='sd', # represententa el promedio y la desviación estándar
boxpoints='suspectedoutliers', # sólo outliers sospechosos
marker=dict(
color='rgba(219, 64, 82, 0.6)',
outliercolor='rgb(8,81,156)',
line=dict(
outliercolor='rgb(8,81,156)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
),
row=1, col=2
)
fig.update_layout(height=600, width=1200, title_text="ÁMBITO DE LA EMPRESA VS. INGRESOS",
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None, showlegend=False,
xaxis = {'categoryorder':'total descending'})
fig.show()
Se observa que quienes trabajan en el ámbito estatal perciben un 31% más de ingresos que aquellos que se desempeñan en el ámbito privado. Con respecto a la categoría otros, podemos suponer que incluye otras organizaciones como por ejemplo ONGs. Cabe aclarar que, según lo visto en el análisis univariado, este grupo solo representa el 1,51% de todos los trabajadores del dataset.
fig = px.violin(df_1,
x="Tamaño_empr_2",
y= 'Ingresos',
title= 'TAMAÑO DE LA EMPRESA VS. INGRESOS'
)
fig.update_layout(
height = 500,
width = 700,
title={
'xanchor': 'center',
'yanchor': 'top',
'y':0.97,
'x':0.5,
},
yaxis_title = 'Ingresos en miles de pesos argentinos',
xaxis_title = None,
xaxis = {'categoryorder':'total descending'}
)
fig.show()
Vemos que a mayor tamaño de la empresa, mayores son los ingresos percibidos por los trabajadores, siendo la brecha salarial promedio entre empresas grandes y microempresas igual a 218%.
fig= px.histogram(df_1,
y='Lugar_trab',
x='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s'
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 500,
width = 700,
title={
'text': 'LUGAR FÍSICO DE TRABAJO VS. INGRESOS',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de lugar de trabajo',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Los tres lugares de trabajo que presentan mayores ingresos en promedio son:
Por su parte, quienes declaran trabajar en "7-Vivienda del socio o del patrón" presentan el menor ingreso, siendo que en promedio alguien de la categoría ubicada en primer lugar del gráfico gana 2,4 veces más.
fig= px.histogram(df_1,
y='ACTIV_ECON',
x='Ingresos',
color='CAT_ECON',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
color_discrete_sequence= px.colors.sequential.Plasma_r
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 1300,
width = 900,
title={
'text': 'ACTIVIDAD ECONÓMICA DE LA EMPRESA VS. INGRESOS (Color según Categoría Económica)',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de aglomerado',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Las tres actividades con el ingreso promedio más alto (cercano a 130 mil pesos) son:
Las tres actividades con el ingreso promedio más bajo (menor a 30 mil pesos) son:
A continuación graficaremos las Categorías Económicas (recordemos que agrupan a las diferentes actividades económicas).
fig= px.histogram(df_1,
y='CAT_ECON',
x='Ingresos',
hover_name='Ingresos',
histfunc='avg', text_auto='.2s',
)
fig.update_traces(
textposition = 'outside'
) #modficar todo lo relacionado con los trazos
fig.update_layout(
height = 800,
width = 800,
title={
'text': 'CATEGORÍA ECONÓMICA DE LA EMPRESA VS. INGRESOS',
},
yaxis = {'categoryorder':'total ascending'},
xaxis_title = 'Ingresos en miles de pesos argentinos',
yaxis_title = 'Código de categoría económica',
plot_bgcolor='white',
font=dict(
family="Calibri",
size=14,
color="black")
)
Al agrupar las actividades por categorías, aparece como primera en cuanto al monto de ingreso promedio de los trabajadores, la categoría "K- Actividades Financieras y de Seguros".
En segundo lugar, con el ingreso promedio apenas 2% inferior a la primera, se ubica la categoría "J- Información y Comunicación".
En tercer lugar se ubica la categoría D, cuyas actividades están relacionadas a Suministro de Electricidad, Gas, Vapor y Aire Acondicionado, con un ingreso promedio 8% menor a la categoría K.
En último lugar, en coincidencia con el gráfico anterior, se encuentra la categoría T, que engloba las actividades relacionadas con el empleo doméstico.
Recordemos cuál es la Hipótesis 9 planteada:
Podemos decir que esta hipótesis parece cierta. En principio, perciben mayores ingresos quienes trabajan en el ámbito estatal, en empresas grandes y aquellos que tienen como lugar físico de trabajo un vehículo (terrestre/aéreo/marítimo).
No está tan claro el comportamiento de los ingresos en relación al tipo de actividad económica de la empresa, ya que no hay una actividad o categoría económica que destaque demasiado por sobre las demás.
Lo que sí se puede ver es que el rubro del empleo doméstico en todos los gráficos de ingresos se ubica en último lugar.
Esta etapa consiste en preparar las variables para introducirlas en nuestro modelo de Machine Learning. Repasaremos de qué tipo es cada una de las variables y qué transformación corresponde hacerle.
#Creamos una copia del dataset proveniente del Data Wrangling
df_1b=df_1a.copy()
df_1b.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null object 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 Sexo 46241 non-null object 4 Edad 46241 non-null int64 5 NIVEL_ED_2 46241 non-null object 6 NIVEL_ED 46241 non-null object 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null object 9 Cant_Ocup 46241 non-null int64 10 Horas_sem 46241 non-null float64 11 INTENSI 46241 non-null object 12 Tipo_empr 46241 non-null object 13 Cod_activ 46241 non-null int64 14 Tamaño_empr 46241 non-null object 15 Cod_Ocup 46241 non-null int64 16 Lugar_trab 46241 non-null int64 17 Ingresos 46241 non-null int64 18 Tamaño_empr_2 46241 non-null object 19 CARACTER_OCUP 46241 non-null object 20 JERARQUIA_OCUP 46241 non-null object 21 TECNOLOGIA_OCUP 46241 non-null object 22 CALIFICACION_OCUP 46241 non-null object 23 ACTIV_ECON 46241 non-null object 24 CAT_ECON 46241 non-null object dtypes: float64(1), int64(8), object(16) memory usage: 9.2+ MB
Clasificando nuevamente las variables que tenemos, en función de cómo ingresarlas al modelo:
Variables numéricas (Ingresan al modelo como están):
Variables categóricas ordinales (Aplicaremos Label Encoding-LE):
Variables categóricas no ordinales (Aplicaremos OneHotEncoding- OHE):
Variables categóricas no ordinales (No aplicaremos OHE):
Para estas variables, dada la gran cantidad de categorías que tiene cada una, no aplicaremos OHE.
df_1b['Tipo_empr'].value_counts()
0.0 28485 Privada 13116 Estatal 4368 Otro 272 Name: Tipo_empr, dtype: int64
#Aplicamos LE a la columna
df_1b['Tipo_empr']=df_1b['Tipo_empr'].replace({'Otro':1,'Privada':2,'Estatal':3})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['Tipo_empr'].value_counts()
0.0 28485 2.0 13116 3.0 4368 1.0 272 Name: Tipo_empr, dtype: int64
df_1b['CAT_OCUP'].value_counts()
0 27454 Obrero o empleado 14162 Cuenta Propia 3966 Patron 547 Trabajador fliar s/remun 111 Ns/Nr 1 Name: CAT_OCUP, dtype: int64
#Aplicamos LE a la columna
df_1b['CAT_OCUP']=df_1b['CAT_OCUP'].replace({'Ns/Nr':0, 'Trabajador fliar s/remun':0, 'Cuenta Propia':1, 'Obrero o empleado':2,'Patron':3})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['CAT_OCUP'].value_counts()
0 27566 2 14162 1 3966 3 547 Name: CAT_OCUP, dtype: int64
df_1b['NIVEL_ED'].value_counts()
3-Secundario incompl 9808 4-Secundario compl 9166 1-Primario incompl 6880 6-Superior universit compl 5766 2-Primario compl 5542 5-Superior universit incompl 5450 0-Sin instrucción 3629 Name: NIVEL_ED, dtype: int64
#Aplicamos LE a la columna NIVEL_ED
df_1b['NIVEL_ED']=df_1b['NIVEL_ED'].replace({'0-Ns/Nr':0,'1-Primario incompl':1,'2-Primario compl':2,
'3-Secundario incompl':3,'4-Secundario compl':4,'5-Superior universit incompl':5,
'6-Superior universit compl':6,'0-Sin instrucción':0})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['NIVEL_ED'].value_counts()
3 9808 4 9166 1 6880 6 5766 2 5542 5 5450 0 3629 Name: NIVEL_ED, dtype: int64
df_1b['NIVEL_ED_2'].value_counts()
4-Secundario 18022 2-Primario 11659 7-Universitario 6560 6-Terciario 4383 1-Jardín/preescolar/Sin instr. 3629 3-EGB 855 5-Polimodal 586 0-Educación especial 274 8-Posgrado universitario 269 99 4 Name: NIVEL_ED_2, dtype: int64
#Aplicamos LE a la columna NIVEL_ED_2
df_1b['NIVEL_ED_2']=df_1b['NIVEL_ED_2'].replace({'0-Educación especial':0,'1-Jardín/preescolar/Sin instr.':1, '1-Jardín/preescolar/Sin instr.':1,'2-Primario':2,
'3-EGB':3,'4-Secundario':4,'5-Polimodal':5,
'6-Terciario':6,'7-Universitario':7,'8-Posgrado universitario':8, 99:0})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['NIVEL_ED_2'].value_counts()
4 18022 2 11659 7 6560 6 4383 1 3629 3 855 5 586 0 278 8 269 Name: NIVEL_ED_2, dtype: int64
df_1b['INTENSI'].value_counts()
0.0 28485 2-Ocupado pleno 9375 3-Sobreocupado 4840 1-Subocupado 1882 2-Ocup q no trabajó ult sem 1659 Name: INTENSI, dtype: int64
#Aplicamos LE a la columna INTENSI
df_1b['INTENSI']=df_1b['INTENSI'].replace({'1-Subocupado':1,'2-Ocupado pleno':2,'3-Sobreocupado':3,'2-Ocup q no trabajó ult sem':2})
#Convertimos la columna a tipo número entero
df_1b['INTENSI']= df_1b['INTENSI'].astype(int)
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['INTENSI'].value_counts()
0 28485 2 11034 3 4840 1 1882 Name: INTENSI, dtype: int64
df_1b['Tamaño_empr'].value_counts()
0) Ns/Nr 31709 1) 1 pers 2944 2) 2 pers 1591 9) 41 a 100 pers 1417 6) 6 a 10 pers 1379 7) 11 a 25 pers 1277 8) 26 a 40 pers 1254 3) 3 pers 967 10) 101 a 200 pers 958 12) Más de 500 pers 911 11) 201 a 500 pers 699 4) 4 pers 591 5) 5 pers 544 Name: Tamaño_empr, dtype: int64
#Aplicamos LE a la columna
df_1b['Tamaño_empr']=df_1b['Tamaño_empr'].replace({'1) 1 pers':1,'2) 2 pers':2,'3) 3 pers':3,'4) 4 pers':4,'5) 5 pers':5,
'6) 6 a 10 pers':6, '7) 11 a 25 pers':7, '8) 26 a 40 pers':8, '9) 41 a 100 pers':9,
'10) 101 a 200 pers':10, '11) 201 a 500 pers':11, '12) Más de 500 pers':12, '0) Ns/Nr':0, '0) Ns/Nr':0})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['Tamaño_empr'].value_counts()
0 31709 1 2944 2 1591 9 1417 6 1379 7 1277 8 1254 3 967 10 958 12 911 11 699 4 591 5 544 Name: Tamaño_empr, dtype: int64
df_1b['Tamaño_empr_2'].value_counts()
0) Ns/Nr 31709 Microempresa 6637 Pequeña 3910 Mediana 2375 Grande 1610 Name: Tamaño_empr_2, dtype: int64
#Aplicamos LE a la columna
df_1b['Tamaño_empr_2']=df_1b['Tamaño_empr_2'].replace({'0) Ns/Nr':0,'Microempresa':1, 'Pequeña':2, 'Mediana':3, 'Grande':4})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['Tamaño_empr_2'].value_counts()
0 31709 1 6637 2 3910 3 2375 4 1610 Name: Tamaño_empr_2, dtype: int64
df_1b['JERARQUIA_OCUP'].value_counts()
0 28487 Asalariados 12831 Cuenta propia 3723 Direccion 736 Jefes 383 Ns/Nr 81 Name: JERARQUIA_OCUP, dtype: int64
#Aplicamos LE a la columna
df_1b['JERARQUIA_OCUP']=df_1b['JERARQUIA_OCUP'].replace({'Direccion':4,'Cuenta propia':1,'Jefes':3,'Asalariados':2,'Ns/Nr':0})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['JERARQUIA_OCUP'].value_counts()
0 28568 2 12831 1 3723 4 736 3 383 Name: JERARQUIA_OCUP, dtype: int64
df_1b['TECNOLOGIA_OCUP'].value_counts()
0 28487 Sin op. máq. 10400 Op. sist. informatizados 4612 Con op. máq. 1542 Ns/Nr 1200 Name: TECNOLOGIA_OCUP, dtype: int64
#Aplicamos LE a la columna
df_1b['TECNOLOGIA_OCUP']=df_1b['TECNOLOGIA_OCUP'].replace({'Ns/Nr':0,'Sin op. máq.':1,'Con op. máq.':2,'Op. sist. informatizados':3})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['TECNOLOGIA_OCUP'].value_counts()
0 29687 1 10400 3 4612 2 1542 Name: TECNOLOGIA_OCUP, dtype: int64
df_1b['CALIFICACION_OCUP'].value_counts()
0 28485 Operativo 9299 No calificado 3807 Técnicos 3175 Profesionales 1390 Ns/Nr 85 Name: CALIFICACION_OCUP, dtype: int64
#Aplicamos LE a la columna
df_1b['CALIFICACION_OCUP']=df_1b['CALIFICACION_OCUP'].replace({'Profesionales':4,'Técnicos':3,'Operativo':2,'No calificado':1, 'Ns/Nr':0, '0':0})
#Verificamos que los valores se hayan reemplazado correctamente
df_1b['CALIFICACION_OCUP'].value_counts()
0 28570 2 9299 1 3807 3 3175 4 1390 Name: CALIFICACION_OCUP, dtype: int64
Antes de aplicar OHE (que incrementa en gran número las columnas del dataset), queremos averiguar si aumentó la correlación entre las variables numéricas y la variable objetivo: 'Ingresos', con respecto a la Matriz de correlación 1.
df_1b.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null object 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 Sexo 46241 non-null object 4 Edad 46241 non-null int64 5 NIVEL_ED_2 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 Cant_Ocup 46241 non-null int64 10 Horas_sem 46241 non-null float64 11 INTENSI 46241 non-null int64 12 Tipo_empr 46241 non-null float64 13 Cod_activ 46241 non-null int64 14 Tamaño_empr 46241 non-null int64 15 Cod_Ocup 46241 non-null int64 16 Lugar_trab 46241 non-null int64 17 Ingresos 46241 non-null int64 18 Tamaño_empr_2 46241 non-null int64 19 CARACTER_OCUP 46241 non-null object 20 JERARQUIA_OCUP 46241 non-null int64 21 TECNOLOGIA_OCUP 46241 non-null int64 22 CALIFICACION_OCUP 46241 non-null int64 23 ACTIV_ECON 46241 non-null object 24 CAT_ECON 46241 non-null object dtypes: float64(2), int64(17), object(6) memory usage: 9.2+ MB
df_1c=df_1b.copy()
df_1c.columns
Index(['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'Edad', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'Cant_Ocup', 'Horas_sem', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Ingresos', 'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP',
'TECNOLOGIA_OCUP', 'CALIFICACION_OCUP', 'ACTIV_ECON', 'CAT_ECON'],
dtype='object')
df_1c.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null object 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 Sexo 46241 non-null object 4 Edad 46241 non-null int64 5 NIVEL_ED_2 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 Cant_Ocup 46241 non-null int64 10 Horas_sem 46241 non-null float64 11 INTENSI 46241 non-null int64 12 Tipo_empr 46241 non-null float64 13 Cod_activ 46241 non-null int64 14 Tamaño_empr 46241 non-null int64 15 Cod_Ocup 46241 non-null int64 16 Lugar_trab 46241 non-null int64 17 Ingresos 46241 non-null int64 18 Tamaño_empr_2 46241 non-null int64 19 CARACTER_OCUP 46241 non-null object 20 JERARQUIA_OCUP 46241 non-null int64 21 TECNOLOGIA_OCUP 46241 non-null int64 22 CALIFICACION_OCUP 46241 non-null int64 23 ACTIV_ECON 46241 non-null object 24 CAT_ECON 46241 non-null object dtypes: float64(2), int64(17), object(6) memory usage: 9.2+ MB
#Analizaremos la correlación entre las variables numéricas
correlations2= df_1c.corr()
indx=correlations2.index
#Para visualizar la matriz de correlación
plt.figure(figsize=(16,12))
sns.heatmap(df_1c[indx].corr(),annot=True,cmap="YlGnBu")
<AxesSubplot:>
Al inicio, en la Matriz de correlación 1, sin ninguna transformación de las columnas del dataframe, la variable que más relación tendría con los ingresos "P21" sería la categoría ocupacional "CAT_OCUP", con un coeficiente de correlación igual a 0,6.
Una vez realizada la limpieza y etiquetado de algunas columnas, vemos cómo han aumentado los valores de correlación, comparando las distitas variables con la variable Ingresos.
df_1b.columns
Index(['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'Edad', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'Cant_Ocup', 'Horas_sem', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Ingresos', 'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP',
'TECNOLOGIA_OCUP', 'CALIFICACION_OCUP', 'ACTIV_ECON', 'CAT_ECON'],
dtype='object')
#Variables a aplicar OnheHotEncoding
OH_Encodear=['REGION','MAS_500','Sexo','CAT_ECON']
#Aplicamos OHE
df_1b = pd.get_dummies(df_1b, columns= OH_Encodear, drop_first=False)
df_1b.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 52 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 AGLOMERADO 46241 non-null int64 1 Edad 46241 non-null int64 2 NIVEL_ED_2 46241 non-null int64 3 NIVEL_ED 46241 non-null int64 4 ESTADO 46241 non-null int64 5 CAT_OCUP 46241 non-null int64 6 Cant_Ocup 46241 non-null int64 7 Horas_sem 46241 non-null float64 8 INTENSI 46241 non-null int64 9 Tipo_empr 46241 non-null float64 10 Cod_activ 46241 non-null int64 11 Tamaño_empr 46241 non-null int64 12 Cod_Ocup 46241 non-null int64 13 Lugar_trab 46241 non-null int64 14 Ingresos 46241 non-null int64 15 Tamaño_empr_2 46241 non-null int64 16 CARACTER_OCUP 46241 non-null object 17 JERARQUIA_OCUP 46241 non-null int64 18 TECNOLOGIA_OCUP 46241 non-null int64 19 CALIFICACION_OCUP 46241 non-null int64 20 ACTIV_ECON 46241 non-null object 21 REGION_CUYO 46241 non-null uint8 22 REGION_GBA 46241 non-null uint8 23 REGION_NEA 46241 non-null uint8 24 REGION_NOA 46241 non-null uint8 25 REGION_PAMPEANA 46241 non-null uint8 26 REGION_PATAGONIA 46241 non-null uint8 27 MAS_500_N 46241 non-null uint8 28 MAS_500_S 46241 non-null uint8 29 Sexo_F 46241 non-null uint8 30 Sexo_M 46241 non-null uint8 31 CAT_ECON_A 46241 non-null uint8 32 CAT_ECON_C 46241 non-null uint8 33 CAT_ECON_D 46241 non-null uint8 34 CAT_ECON_E 46241 non-null uint8 35 CAT_ECON_F 46241 non-null uint8 36 CAT_ECON_G 46241 non-null uint8 37 CAT_ECON_H 46241 non-null uint8 38 CAT_ECON_I 46241 non-null uint8 39 CAT_ECON_J 46241 non-null uint8 40 CAT_ECON_K 46241 non-null uint8 41 CAT_ECON_L 46241 non-null uint8 42 CAT_ECON_M 46241 non-null uint8 43 CAT_ECON_N 46241 non-null uint8 44 CAT_ECON_O 46241 non-null uint8 45 CAT_ECON_P 46241 non-null uint8 46 CAT_ECON_Q 46241 non-null uint8 47 CAT_ECON_R 46241 non-null uint8 48 CAT_ECON_S 46241 non-null uint8 49 CAT_ECON_T 46241 non-null uint8 50 CAT_ECON_U 46241 non-null uint8 51 CAT_ECON_W 46241 non-null uint8 dtypes: float64(2), int64(17), object(2), uint8(31) memory usage: 9.1+ MB
Como se mencionó más arriba, transformaremos a numéricas las columnas "CARACTER OCUPACIONAL" y "ACTIVIDAD ECONÓMICA". Cada una contiene códigos pero que se encuentran en formato string.
df_1b.describe(include=object)
| CARACTER_OCUP | ACTIV_ECON | |
|---|---|---|
| count | 46241 | 46241 |
| unique | 52 | 78 |
| top | 0 | 0 |
| freq | 28487 | 28639 |
df_1b[['CARACTER_OCUP','ACTIV_ECON']]=df_1b[['CARACTER_OCUP','ACTIV_ECON']].astype(int)
Emplearemos la técnica de Isolation Forest.
isolation_forest = IsolationForest(contamination=0.15)
isolation_forest.fit(df_1b)
IsolationForest(contamination=0.15)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
IsolationForest(contamination=0.15)
y_outlier = isolation_forest.predict(df_1b)
df_1b['is_outlier'] = y_outlier
df_1b.sample(10)
| AGLOMERADO | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | Horas_sem | INTENSI | Tipo_empr | ... | CAT_ECON_N | CAT_ECON_O | CAT_ECON_P | CAT_ECON_Q | CAT_ECON_R | CAT_ECON_S | CAT_ECON_T | CAT_ECON_U | CAT_ECON_W | is_outlier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35528 | 13 | 15 | 3 | 3 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 33656 | 26 | 18 | 7 | 5 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 38003 | 31 | 58 | 4 | 4 | 1 | 1 | 1 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7170 | 17 | 73 | 2 | 1 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 14153 | 15 | 43 | 7 | 6 | 1 | 2 | 1 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 28781 | 5 | 45 | 7 | 5 | 1 | 2 | 1 | 48.0 | 3 | 3.0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 34087 | 5 | 64 | 4 | 4 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 23111 | 22 | 34 | 4 | 4 | 1 | 1 | 1 | 36.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | -1 |
| 4779 | 17 | 65 | 6 | 6 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 7554 | 25 | 18 | 4 | 3 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
10 rows × 53 columns
df_1b['is_outlier'].value_counts()
1 39305 -1 6936 Name: is_outlier, dtype: int64
outliers= df_1b[df_1b.is_outlier == -1]
outliers
| AGLOMERADO | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | Horas_sem | INTENSI | Tipo_empr | ... | CAT_ECON_N | CAT_ECON_O | CAT_ECON_P | CAT_ECON_Q | CAT_ECON_R | CAT_ECON_S | CAT_ECON_T | CAT_ECON_U | CAT_ECON_W | is_outlier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14 | 42 | 6 | 6 | 1 | 2 | 1 | 50.0 | 3 | 3.0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 12 | 31 | 56 | 4 | 4 | 1 | 2 | 1 | 40.0 | 2 | 3.0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 14 | 31 | 26 | 6 | 6 | 1 | 2 | 0 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 16 | 31 | 65 | 4 | 4 | 1 | 2 | 1 | 35.0 | 2 | 3.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 20 | 13 | 30 | 3 | 3 | 1 | 2 | 0 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 49673 | 93 | 35 | 6 | 5 | 1 | 2 | 1 | 35.0 | 2 | 3.0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1 |
| 49675 | 17 | 40 | 4 | 4 | 1 | 2 | 1 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | -1 |
| 49693 | 32 | 42 | 8 | 6 | 1 | 1 | 1 | 40.0 | 2 | 2.0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 49695 | 32 | 36 | 7 | 6 | 1 | 2 | 2 | 40.0 | 3 | 3.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 49704 | 2 | 34 | 6 | 6 | 1 | 2 | 1 | 72.0 | 3 | 3.0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
6936 rows × 53 columns
Eliminaremos estos outliers que indica Isolation Forest.
df_sin_outliers = df_1b[df_1b.is_outlier == 1]
Haremos competir 5 modelos de regresión, mediante validación cruzada. Dividiremos el conjunto de datos de entrenamiento en 6 grupos o folds. Se seleccionará el que mejores predicciones realice, en función de las métricas de evaluación elegidas.
En primer lugar definimos las variables independientes "X" y la variable objetivo "y".
X = df_sin_outliers.drop(['Ingresos','is_outlier'], axis=1)
y = df_sin_outliers[['Ingresos']]
Split de la data.
# Realizamos la primera partición
X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
# Realizamos la segunda partición
X_train, X_valid, y_train, y_valid = train_test_split(X_train_temp, y_train_temp,
test_size=0.3, random_state=42)
Definiremos una función que compare los resultados del CV del conjunto de modelos a competir.
# Definimos una función que compare el desempeño de CV en el conjunto de modelos a competir
def cv_comparison(modelos, X, y, cv):
# Creamos un DataFrame para los promedios y una lista para todas las métricas
cv_metricas = pd.DataFrame()
maes = []
mses = []
r2s = []
# Hacemos un loop en los modelos, corremos CV, agregamos los puntajes promedio obtenidos al DataFrame
# y los puntajes de todos los CVs a la lista
for modelo in modelos:
mae = -np.round(cross_val_score(modelo, X, y, scoring='neg_mean_absolute_error', cv=cv), 6)
maes.append(mae)
mae_avg = round(mae.mean(), 0)
mse = -np.round(cross_val_score(modelo, X, y, scoring='neg_mean_squared_error', cv=cv), 6)
mses.append(mse)
mse_avg = round(mse.mean(), 0)
r2 = np.round(cross_val_score(modelo, X, y, scoring='r2', cv=cv), 6)
r2s.append(r2)
r2_avg = round(r2.mean(), 4)
cv_metricas[str(modelo)] = [mae_avg, mse_avg, r2_avg]
cv_metricas.index = ['Mean Absolute Error', 'Mean Squared Error', 'R^2']
return cv_metricas, maes, mses, r2s
Creamos los modelos a ser evaluados.
#Asignamos una variable a cada modelo
RLM = LinearRegression()
GBR = GradientBoostingRegressor()
HGBR = HistGradientBoostingRegressor()
CBR = CatBoostRegressor()
XGBR = XGBRegressor()
#Ponemos los modelos es una lista que emplearemos en Cross-Validation
modelos=[RLM, GBR, HGBR, CBR, XGBR]
Aplicamos Cross-Validation a los modelos
#Corremos la función arriba definida
cv_metricas, maes, mses, r2s = cv_comparison(modelos, X_train_temp, y_train_temp, 6)
cv_metricas.columns
Index(['LinearRegression()', 'GradientBoostingRegressor()',
'HistGradientBoostingRegressor()',
'<catboost.core.CatBoostRegressor object at 0x7f9be4f98b50>',
'XGBRegressor(base_score=None, booster=None, callbacks=None,\n colsample_bylevel=None, colsample_bynode=None,\n colsample_bytree=None, early_stopping_rounds=None,\n enable_categorical=False, eval_metric=None, feature_types=None,\n gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n interaction_constraints=None, learning_rate=None, max_bin=None,\n max_cat_threshold=None, max_cat_to_onehot=None,\n max_delta_step=None, max_depth=None, max_leaves=None,\n min_child_weight=None, missing=nan, monotone_constraints=None,\n n_estimators=100, n_jobs=None, num_parallel_tree=None,\n predictor=None, random_state=None, ...)'],
dtype='object')
#Renombramos las columnas de la tabla de resultados
cv_metricas = cv_metricas.rename(columns={'LinearRegression()':'RLM','GradientBoostingRegressor()': 'GBR', 'HistGradientBoostingRegressor()':'HGBR',
'<catboost.core.CatBoostRegressor object at 0x7f9be4f98b50>':'CBR',
'XGBRegressor(base_score=None, booster=None, callbacks=None,\n colsample_bylevel=None, colsample_bynode=None,\n colsample_bytree=None, early_stopping_rounds=None,\n enable_categorical=False, eval_metric=None, feature_types=None,\n gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n interaction_constraints=None, learning_rate=None, max_bin=None,\n max_cat_threshold=None, max_cat_to_onehot=None,\n max_delta_step=None, max_depth=None, max_leaves=None,\n min_child_weight=None, missing=nan, monotone_constraints=None,\n n_estimators=100, n_jobs=None, num_parallel_tree=None,\n predictor=None, random_state=None, ...)':'XGBR'})
cv_metricas.columns
Index(['RLM', 'GBR', 'HGBR', 'CBR', 'XGBR'], dtype='object')
#Modificamos el formato de las columnas
cv_metricas['RLM'] = cv_metricas['RLM'].map('{:,.4f}'.format)
cv_metricas['GBR'] = cv_metricas['GBR'].map('{:,.4f}'.format)
cv_metricas['HGBR'] = cv_metricas['HGBR'].map('{:,.4f}'.format)
cv_metricas['CBR'] = cv_metricas['CBR'].map('{:,.4f}'.format)
cv_metricas['XGBR'] = cv_metricas['XGBR'].map('{:,.4f}'.format)
cv_metricas
| RLM | GBR | HGBR | CBR | XGBR | |
|---|---|---|---|---|---|
| Mean Absolute Error | 5,874.0000 | 3,900.0000 | 3,714.0000 | 3,767.0000 | 3,895.0000 |
| Mean Squared Error | 144,315,692.0000 | 108,154,847.0000 | 102,280,094.0000 | 102,319,507.0000 | 109,374,447.0000 |
| R^2 | 0.7637 | 0.8229 | 0.8330 | 0.8323 | 0.8206 |
Vemos que tanto el modelo HistGradientBoostingRegressor como CatBoostRegressor perfoman de manera similar. Por un lado, HGBR presenta el menor MAE promedio, mientras que CBR tiene mejor R2.
A continuación veremos el valor de estas métricas para cada uno de los folds del Cross-Validation.
# Creamos un DataFrame para todos los R2
r2_comp = pd.DataFrame(r2s, index=cv_metricas.columns, columns=['1er Fold', '2do Fold', '3er Fold',
'4to Fold', '5to Fold','6to Fold'])
# Agregamos una columna para los promedios
r2_comp['Promedio'] = np.round(r2_comp.mean(axis=1),4)
r2_comp
| 1er Fold | 2do Fold | 3er Fold | 4to Fold | 5to Fold | 6to Fold | Promedio | |
|---|---|---|---|---|---|---|---|
| RLM | 0.766674 | 0.738987 | 0.762353 | 0.772504 | 0.775893 | 0.765555 | 0.7637 |
| GBR | 0.825770 | 0.805149 | 0.825473 | 0.832264 | 0.834672 | 0.814192 | 0.8229 |
| HGBR | 0.832078 | 0.819088 | 0.833993 | 0.840244 | 0.846470 | 0.825866 | 0.8330 |
| CBR | 0.833862 | 0.819295 | 0.830218 | 0.839403 | 0.846525 | 0.824659 | 0.8323 |
| XGBR | 0.819112 | 0.810363 | 0.818531 | 0.828417 | 0.835951 | 0.811410 | 0.8206 |
De la tabla de valores de R^2, se observa que, para un mismo modelo, los valores de los distintos folds son muy similares entre sí, lo que habla de modelos robustos.
# Creamos un DataFrame para todos los R2
MAE_comp = pd.DataFrame(maes, index=cv_metricas.columns, columns=['1er Fold', '2do Fold', '3er Fold',
'4to Fold', '5to Fold','6to Fold'])
# Agregamos una columna para los promedios
MAE_comp['Promedio'] = np.round(MAE_comp.mean(axis=1),0)
MAE_comp
| 1er Fold | 2do Fold | 3er Fold | 4to Fold | 5to Fold | 6to Fold | Promedio | |
|---|---|---|---|---|---|---|---|
| RLM | 5693.950188 | 6163.287951 | 5822.793999 | 5778.938773 | 5900.426440 | 5885.612813 | 5874.0 |
| GBR | 3664.065427 | 4194.394384 | 3793.326783 | 3751.081116 | 3955.241690 | 4044.364606 | 3900.0 |
| HGBR | 3474.104950 | 4000.653980 | 3690.380755 | 3582.712299 | 3711.584740 | 3822.063450 | 3714.0 |
| CBR | 3540.076473 | 4023.559776 | 3784.678796 | 3656.113523 | 3726.773502 | 3872.930444 | 3767.0 |
| XGBR | 3669.414008 | 4111.165616 | 3918.492832 | 3742.888293 | 3900.955136 | 4028.045184 | 3895.0 |
Para el caso de los valores de MAE se da una similar situación a lo que ocurre para los valores de R^2, dándonos la pauta de que el modelo es bueno.
Ahora que ya conocemos los dos modelos que mejor performan, HistGradientBoostingRegressor y CatBoostRegressor, los entrenaremos mediante validación simple, para comparar sus predicciones con los datos reales del y_test.
Asignamos una variable a los modelos.
HGBR2 = HistGradientBoostingRegressor()
CBR2 = CatBoostRegressor()
Entrenamiento de los modelos.
# Recordemos la primera partición realizada
# X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
HGBR2.fit(X_train_temp,y_train_temp)
CBR2.fit(X_train_temp,y_train_temp)
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<catboost.core.CatBoostRegressor at 0x7f9be4c7a790>
p_HGBR =HGBR2.predict(X_test)
p_CBR =CBR2.predict(X_test)
tabla_resumen = X_test.copy()
tabla_resumen['respuestas'] = y_test
#Añadimos las predicciones de los modelos a nuestra tabla
tabla_resumen['p_HGBR'] = p_HGBR
tabla_resumen['p_CBR'] = p_CBR
tabla_resumen
| AGLOMERADO | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | Horas_sem | INTENSI | Tipo_empr | ... | CAT_ECON_P | CAT_ECON_Q | CAT_ECON_R | CAT_ECON_S | CAT_ECON_T | CAT_ECON_U | CAT_ECON_W | respuestas | p_HGBR | p_CBR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13517 | 20 | 16 | 4 | 3 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | -195.325696 |
| 36330 | 29 | 32 | 7 | 5 | 1 | 2 | 1 | 25.0 | 2 | 3.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50000 | 46547.600294 | 42080.156780 |
| 48601 | 8 | 39 | 4 | 4 | 1 | 2 | 1 | 30.0 | 2 | 3.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54000 | 45631.026835 | 44817.282498 |
| 29269 | 6 | 3 | 1 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | -72.917524 |
| 15882 | 33 | 50 | 4 | 3 | 1 | 1 | 1 | 40.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60000 | 46862.525697 | 51288.573215 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 38321 | 15 | 33 | 2 | 2 | 1 | 2 | 2 | 24.0 | 2 | 2.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30000 | 21483.130584 | 23915.275458 |
| 39014 | 5 | 68 | 4 | 4 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | 121.709987 |
| 10365 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | -46.777807 |
| 542 | 33 | 73 | 4 | 4 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | -139.118760 |
| 48011 | 34 | 16 | 4 | 3 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 52.617634 | 21.815456 |
11792 rows × 54 columns
Cálculo de métricas.
1) Mean squared error
print('MSE de HGBR')
print(mean_squared_error(y_test,p_HGBR))
print('MSE de CBR')
print(mean_squared_error(y_test,p_CBR))
MSE de HGBR 100275844.15326901 MSE de CBR 97134507.35625592
2) R2
print('R2 de HGBR')
print(r2_score(y_test,p_HGBR))
print('R2 de CBR')
print(r2_score(y_test,p_CBR))
R2 de HGBR 0.8426245916636309 R2 de CBR 0.8475546839039552
3) MAE
print('MAE de HGBR')
print(mean_absolute_error(y_test,p_HGBR))
print('MAE de CBR')
print(mean_absolute_error(y_test,p_CBR))
MAE de HGBR 3771.316125268828 MAE de CBR 3722.7450014878173
fig = px.scatter(tabla_resumen,
x= tabla_resumen.index,
y= ['respuestas','p_HGBR','p_CBR'],
trendline="lowess",
trendline_options=dict(frac=0.03),
title="Data real vs Predicciones")
fig.data = [t for t in fig.data if t.mode == "lines"]
fig.update_traces(showlegend=True) #trendlines have showlegend=False by default
fig.show()
Reemplazamos los valores negativos de las predicciones:
#Reemplazamos por 0 las predicciones negativas de ambos modelos
p_HGBR = np.where((p_HGBR <0 ), 0, p_HGBR)
p_CBR = np.where((p_HGBR <0 ), 0, p_CBR)
#Reemplazamos por 0 las predicciones negativas de ambos modelos en la tabla
tabla_resumen['p_HGBR'] = np.where((tabla_resumen['p_HGBR'] <0 ), 0, tabla_resumen['p_HGBR'])
tabla_resumen['p_CBR'] = np.where((tabla_resumen['p_CBR'] <0 ), 0, tabla_resumen['p_CBR'])
tabla_resumen['p_HGBR'].value_counts()
52.617634 8062
62.597800 484
0.000000 15
20200.265974 2
11258.851758 2
...
36985.241867 1
15995.463357 1
57252.430351 1
27390.376160 1
21483.130584 1
Name: p_HGBR, Length: 3208, dtype: int64
tabla_resumen['p_CBR'].value_counts()
0.000000 4299
22.304521 76
41.016162 58
97.193368 57
21.524859 42
...
19557.705456 1
86.215854 1
34799.018828 1
25636.296331 1
121.709987 1
Name: p_CBR, Length: 5042, dtype: int64
tabla_resumen['respuestas'].value_counts()
0 8625
30000 247
60000 242
40000 233
50000 212
...
10800 1
8100 1
800 1
38500 1
125000 1
Name: respuestas, Length: 188, dtype: int64
Graficamos nuevamente, ahora ya sin los valores negativos:
fig = px.scatter(tabla_resumen,
x= tabla_resumen.index,
y= ['respuestas','p_HGBR','p_CBR'],
trendline="lowess",
trendline_options=dict(frac=0.03),
title="Data real vs Predicciones")
fig.data = [t for t in fig.data if t.mode == "lines"]
fig.update_traces(showlegend=True)
fig.show()
Del gráfico se observa que ambos modelos performan de forma similar. Sin embargo, al analizar la frecuencia de las predicciones, notamos que HistGradientBoostingRegressor presenta muchos valores repetidos, por lo que pareciera ser que tiende al overfitting.
Es por esto que se seleccionará como modelo ganador a Catboost.
Determinaremos la importancia de las variables del modelo Catboost, que fue el que mejor performó.
CBR2.feature_importances_
array([6.54938533e+00, 7.42758336e+00, 1.48349023e+00, 2.68870251e+00,
8.10468383e-01, 6.43143869e-01, 1.21063874e+01, 1.33957994e+01,
2.94682077e+00, 8.86148424e-01, 2.39797624e+00, 8.07136810e+00,
4.19414628e+00, 3.61474570e+00, 2.18719798e+00, 1.39433638e+00,
8.69135060e-01, 5.02143972e+00, 3.99967853e+00, 1.34605926e+00,
3.72670041e-01, 5.15919226e-01, 6.10233110e-01, 2.11748861e+00,
8.02308251e-01, 5.20754134e+00, 4.36818213e-01, 2.00889981e-01,
8.08156822e-01, 9.32136599e-01, 2.99831303e-03, 1.06131474e-01,
1.42617634e-01, 2.77687992e-01, 2.15282015e-01, 4.40418390e-01,
1.20098830e+00, 2.44662259e-01, 4.58068023e-01, 6.81347460e-01,
1.80493908e-01, 1.40706382e-01, 4.55118785e-01, 9.99016569e-01,
1.82511454e-01, 1.32244943e-01, 9.87919824e-02, 2.73898730e-03,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00])
X_train_temp.columns
Index(['AGLOMERADO', 'Edad', 'NIVEL_ED_2', 'NIVEL_ED', 'ESTADO', 'CAT_OCUP',
'Cant_Ocup', 'Horas_sem', 'INTENSI', 'Tipo_empr', 'Cod_activ',
'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab', 'Tamaño_empr_2',
'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP',
'CALIFICACION_OCUP', 'ACTIV_ECON', 'REGION_CUYO', 'REGION_GBA',
'REGION_NEA', 'REGION_NOA', 'REGION_PAMPEANA', 'REGION_PATAGONIA',
'MAS_500_N', 'MAS_500_S', 'Sexo_F', 'Sexo_M', 'CAT_ECON_A',
'CAT_ECON_C', 'CAT_ECON_D', 'CAT_ECON_E', 'CAT_ECON_F', 'CAT_ECON_G',
'CAT_ECON_H', 'CAT_ECON_I', 'CAT_ECON_J', 'CAT_ECON_K', 'CAT_ECON_L',
'CAT_ECON_M', 'CAT_ECON_N', 'CAT_ECON_O', 'CAT_ECON_P', 'CAT_ECON_Q',
'CAT_ECON_R', 'CAT_ECON_S', 'CAT_ECON_T', 'CAT_ECON_U', 'CAT_ECON_W'],
dtype='object')
feature_imp = pd.Series(CBR2.feature_importances_,index=X_train_temp.columns).sort_values(ascending=False)
feature_imp
Horas_sem 13.395799 Cant_Ocup 12.106387 Tamaño_empr 8.071368 Edad 7.427583 AGLOMERADO 6.549385 REGION_PATAGONIA 5.207541 TECNOLOGIA_OCUP 5.021440 Cod_Ocup 4.194146 CALIFICACION_OCUP 3.999679 Lugar_trab 3.614746 INTENSI 2.946821 NIVEL_ED 2.688703 Cod_activ 2.397976 Tamaño_empr_2 2.187198 REGION_NOA 2.117489 NIVEL_ED_2 1.483490 CARACTER_OCUP 1.394336 ACTIV_ECON 1.346059 CAT_ECON_H 1.200988 CAT_ECON_O 0.999017 Sexo_M 0.932137 Tipo_empr 0.886148 JERARQUIA_OCUP 0.869135 ESTADO 0.810468 Sexo_F 0.808157 REGION_PAMPEANA 0.802308 CAT_ECON_K 0.681347 CAT_OCUP 0.643144 REGION_NEA 0.610233 REGION_GBA 0.515919 CAT_ECON_J 0.458068 CAT_ECON_N 0.455119 CAT_ECON_G 0.440418 MAS_500_N 0.436818 REGION_CUYO 0.372670 CAT_ECON_E 0.277688 CAT_ECON_I 0.244662 CAT_ECON_F 0.215282 MAS_500_S 0.200890 CAT_ECON_P 0.182511 CAT_ECON_L 0.180494 CAT_ECON_D 0.142618 CAT_ECON_M 0.140706 CAT_ECON_Q 0.132245 CAT_ECON_C 0.106131 CAT_ECON_R 0.098792 CAT_ECON_A 0.002998 CAT_ECON_S 0.002739 CAT_ECON_T 0.000000 CAT_ECON_U 0.000000 CAT_ECON_W 0.000000 dtype: float64
# Creamos el gráfico de barras horizontales
plt.figure(figsize=(15,15))
sns.barplot(x=feature_imp, y=feature_imp.index)
# Agregamos las etiquetas al gráfico
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features")
plt.legend()
plt.show()
WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
En el gráfico se observa que aproximadamente la mitad de las variables tienen poca relevancia, siendo en su mayoría aquellas que se crearon a partir de OHE, pertenecientes a la categoría "CAT_ECON" (Categoría económica).
Realizaremos nuevamente el entrenamiento del modelo Catboost. Esta vez emplearemos la función Pool que provee Catboost, la cual evita que se requiera realizar OHE a las variables categóricas (es decir a aquellas que no son de tipo numérico). Para verificar que no desciendan los valores de las métricas, lo haremos mediante Cross-Validation.
df_2= df_1c.copy()
df_2.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null object 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null object 3 Sexo 46241 non-null object 4 Edad 46241 non-null int64 5 NIVEL_ED_2 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 Cant_Ocup 46241 non-null int64 10 Horas_sem 46241 non-null float64 11 INTENSI 46241 non-null int64 12 Tipo_empr 46241 non-null float64 13 Cod_activ 46241 non-null int64 14 Tamaño_empr 46241 non-null int64 15 Cod_Ocup 46241 non-null int64 16 Lugar_trab 46241 non-null int64 17 Ingresos 46241 non-null int64 18 Tamaño_empr_2 46241 non-null int64 19 CARACTER_OCUP 46241 non-null object 20 JERARQUIA_OCUP 46241 non-null int64 21 TECNOLOGIA_OCUP 46241 non-null int64 22 CALIFICACION_OCUP 46241 non-null int64 23 ACTIV_ECON 46241 non-null object 24 CAT_ECON 46241 non-null object dtypes: float64(2), int64(17), object(6) memory usage: 9.2+ MB
Eliminaremos la columna CAT_ECON ya que vimos que todas sus variables derivadas del OHE tuvieron muy baja importancia.
#Eliminamos la columna CAT_ECON, que resultó ser de muy baja importancia
#df_2 = df_2.drop(['CAT_ECON'], axis=1)
Emplearemos nuevamente la técnica de Isolation Forest. Pero para ello será necesario que transformemos momentáneamente las columnas tipo texto a númericas.
df_2[['CARACTER_OCUP','ACTIV_ECON']]=df_2[['CARACTER_OCUP','ACTIV_ECON']].astype(int)
df_2['REGION'].value_counts()
PAMPEANA 13550 NOA 10725 PATAGONIA 6211 GBA 5747 CUYO 5156 NEA 4852 Name: REGION, dtype: int64
#Asignamos número a los valores de la columna Region
df_2['REGION']=df_2['REGION'].replace({'GBA':5, 'NOA':1, 'NEA':2,
'CUYO':3,'PAMPEANA':4,'PATAGONIA':6})
df_2['MAS_500'].value_counts()
N 25411 S 20830 Name: MAS_500, dtype: int64
#Asignamos número a los valores de la columna MAS_500
df_2['MAS_500']=df_2['MAS_500'].replace({'N':0, 'S':1})
df_2['Sexo'].value_counts()
F 24440 M 21801 Name: Sexo, dtype: int64
#Asignamos número a los valores de la columna Sexo
df_2['Sexo']=df_2['Sexo'].replace({'F':1, 'M':2})
df_2['CAT_ECON'].value_counts()
W 28639 G 3284 O 2298 C 1898 F 1792 P 1541 T 1274 Q 1145 H 780 S 745 N 686 M 526 I 513 R 329 J 289 K 245 E 107 L 76 D 65 A 8 U 1 Name: CAT_ECON, dtype: int64
#Asignamos número a los valores de la columna CAT_ECON
df_2['CAT_ECON']=df_2['CAT_ECON'].replace({'W':21, 'G':20,'O':19,'C':18,'F':17,'P':16,'T':15,'Q':14,
'H':13, 'S':12,'N':11,'M':10,'I':9,'R':8,'J':7,'K':6,
'E':5, 'L':4,'D':3,'A':2,'U':1
})
df_2.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 46241 entries, 0 to 49705 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 46241 non-null int64 1 AGLOMERADO 46241 non-null int64 2 MAS_500 46241 non-null int64 3 Sexo 46241 non-null int64 4 Edad 46241 non-null int64 5 NIVEL_ED_2 46241 non-null int64 6 NIVEL_ED 46241 non-null int64 7 ESTADO 46241 non-null int64 8 CAT_OCUP 46241 non-null int64 9 Cant_Ocup 46241 non-null int64 10 Horas_sem 46241 non-null float64 11 INTENSI 46241 non-null int64 12 Tipo_empr 46241 non-null float64 13 Cod_activ 46241 non-null int64 14 Tamaño_empr 46241 non-null int64 15 Cod_Ocup 46241 non-null int64 16 Lugar_trab 46241 non-null int64 17 Ingresos 46241 non-null int64 18 Tamaño_empr_2 46241 non-null int64 19 CARACTER_OCUP 46241 non-null int64 20 JERARQUIA_OCUP 46241 non-null int64 21 TECNOLOGIA_OCUP 46241 non-null int64 22 CALIFICACION_OCUP 46241 non-null int64 23 ACTIV_ECON 46241 non-null int64 24 CAT_ECON 46241 non-null int64 dtypes: float64(2), int64(23) memory usage: 9.2 MB
Ahora que todas las columnas son numéricas, podemos aplicar Isolation Forest.
isolation_forest = IsolationForest(contamination=0.15)
isolation_forest.fit(df_2)
IsolationForest(contamination=0.15)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
IsolationForest(contamination=0.15)
y_outlier = isolation_forest.predict(df_2)
df_2['is_outlier'] = y_outlier
df_2.sample(10)
| REGION | AGLOMERADO | MAS_500 | Sexo | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | ... | Lugar_trab | Ingresos | Tamaño_empr_2 | CARACTER_OCUP | JERARQUIA_OCUP | TECNOLOGIA_OCUP | CALIFICACION_OCUP | ACTIV_ECON | CAT_ECON | is_outlier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10893 | 1 | 19 | 0 | 1 | 34 | 6 | 6 | 1 | 2 | 1 | ... | 1 | 30000 | 3 | 41 | 2 | 3 | 3 | 85 | 16 | 1 |
| 12394 | 6 | 91 | 0 | 1 | 24 | 4 | 3 | 0 | 2 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 44738 | 4 | 3 | 0 | 1 | 69 | 2 | 2 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 46852 | 4 | 30 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 2496 | 1 | 23 | 1 | 2 | 13 | 4 | 3 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 18159 | 4 | 4 | 1 | 2 | 85 | 7 | 5 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 27107 | 2 | 15 | 0 | 1 | 45 | 2 | 2 | 1 | 1 | 1 | ... | 6 | 12000 | 1 | 30 | 1 | 1 | 2 | 48 | 20 | -1 |
| 41251 | 6 | 17 | 0 | 2 | 52 | 6 | 6 | 1 | 2 | 1 | ... | 1 | 98000 | 2 | 41 | 2 | 1 | 3 | 85 | 16 | 1 |
| 43889 | 3 | 27 | 1 | 2 | 19 | 4 | 3 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
| 3418 | 5 | 33 | 1 | 1 | 9 | 2 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 |
10 rows × 26 columns
df_2['is_outlier'].value_counts()
1 39305 -1 6936 Name: is_outlier, dtype: int64
outliers= df_2[df_2.is_outlier == -1]
outliers
| REGION | AGLOMERADO | MAS_500 | Sexo | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | ... | Lugar_trab | Ingresos | Tamaño_empr_2 | CARACTER_OCUP | JERARQUIA_OCUP | TECNOLOGIA_OCUP | CALIFICACION_OCUP | ACTIV_ECON | CAT_ECON | is_outlier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4 | 14 | 0 | 1 | 42 | 6 | 6 | 1 | 2 | 1 | ... | 1 | 150000 | 2 | 48 | 2 | 1 | 4 | 84 | 19 | -1 |
| 12 | 6 | 31 | 0 | 2 | 56 | 4 | 4 | 1 | 2 | 1 | ... | 1 | 120000 | 3 | 3 | 4 | 0 | 4 | 84 | 19 | -1 |
| 14 | 6 | 31 | 0 | 2 | 26 | 6 | 6 | 1 | 2 | 0 | ... | 6 | 0 | 2 | 80 | 2 | 3 | 3 | 73 | 10 | -1 |
| 16 | 6 | 31 | 0 | 2 | 65 | 4 | 4 | 1 | 2 | 1 | ... | 6 | 154000 | 3 | 42 | 2 | 3 | 4 | 72 | 10 | -1 |
| 27 | 4 | 13 | 1 | 1 | 45 | 4 | 3 | 1 | 1 | 1 | ... | 9 | 4000 | 1 | 33 | 1 | 1 | 1 | 56 | 9 | -1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 49683 | 4 | 3 | 0 | 1 | 25 | 2 | 2 | 1 | 2 | 1 | ... | 1 | 20000 | 2 | 80 | 2 | 1 | 2 | 23 | 18 | -1 |
| 49693 | 5 | 32 | 1 | 1 | 42 | 8 | 6 | 1 | 1 | 1 | ... | 10 | 100000 | 1 | 30 | 1 | 3 | 3 | 79 | 11 | -1 |
| 49695 | 5 | 32 | 1 | 2 | 36 | 7 | 6 | 1 | 2 | 2 | ... | 6 | 98000 | 2 | 42 | 2 | 3 | 3 | 73 | 10 | -1 |
| 49704 | 4 | 2 | 1 | 1 | 34 | 6 | 6 | 1 | 2 | 1 | ... | 1 | 50000 | 4 | 10 | 2 | 3 | 2 | 84 | 19 | -1 |
| 49705 | 4 | 2 | 1 | 1 | 64 | 2 | 2 | 1 | 2 | 1 | ... | 8 | 30000 | 1 | 56 | 2 | 1 | 1 | 0 | 21 | -1 |
6936 rows × 26 columns
Eliminaremos estos outliers que indica Isolation Forest.
df_sin_outliers2 = df_2[df_2.is_outlier == 1]
#Data a entrenar
df_2 = df_sin_outliers2.drop(['is_outlier'], axis=1)
En primer lugar definimos las variables independientes "X" y la variable objetivo "y".
X2 = df_2.drop(['Ingresos'], axis=1)
y2 = df_2[['Ingresos']]
X2.columns
Index(['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'Edad', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'Cant_Ocup', 'Horas_sem', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP',
'CALIFICACION_OCUP', 'ACTIV_ECON', 'CAT_ECON'],
dtype='object')
Convertimos a tipo string todas las variables categóricas.
X2[['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP',
'CALIFICACION_OCUP', 'ACTIV_ECON','CAT_ECON']] = X2[['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP',
'CALIFICACION_OCUP', 'ACTIV_ECON','CAT_ECON']].astype(str)
X2.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 39305 entries, 1 to 49703 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 REGION 39305 non-null object 1 AGLOMERADO 39305 non-null object 2 MAS_500 39305 non-null object 3 Sexo 39305 non-null object 4 Edad 39305 non-null int64 5 NIVEL_ED_2 39305 non-null object 6 NIVEL_ED 39305 non-null object 7 ESTADO 39305 non-null object 8 CAT_OCUP 39305 non-null object 9 Cant_Ocup 39305 non-null int64 10 Horas_sem 39305 non-null float64 11 INTENSI 39305 non-null object 12 Tipo_empr 39305 non-null object 13 Cod_activ 39305 non-null object 14 Tamaño_empr 39305 non-null object 15 Cod_Ocup 39305 non-null object 16 Lugar_trab 39305 non-null object 17 Tamaño_empr_2 39305 non-null object 18 CARACTER_OCUP 39305 non-null object 19 JERARQUIA_OCUP 39305 non-null object 20 TECNOLOGIA_OCUP 39305 non-null object 21 CALIFICACION_OCUP 39305 non-null object 22 ACTIV_ECON 39305 non-null object 23 CAT_ECON 39305 non-null object dtypes: float64(1), int64(2), object(21) memory usage: 7.5+ MB
Vemos que solo nos quedaron como numéricas las variables Edad, Cantidad de ocupaciones y Horas semanales trabajadas.
#Lista de columnas categóricas
categoricalcolumns = ['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'NIVEL_ED_2',
'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'INTENSI',
'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab',
'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP',
'CALIFICACION_OCUP', 'ACTIV_ECON','CAT_ECON']
print("Names of categorical columns : ", categoricalcolumns)
#Obtener ubicación de las columnas categoricas
cat_features = [X2.columns.get_loc(col) for col in categoricalcolumns]
print("Location of categorical columns : ",cat_features)
Names of categorical columns : ['REGION', 'AGLOMERADO', 'MAS_500', 'Sexo', 'NIVEL_ED_2', 'NIVEL_ED', 'ESTADO', 'CAT_OCUP', 'INTENSI', 'Tipo_empr', 'Cod_activ', 'Tamaño_empr', 'Cod_Ocup', 'Lugar_trab', 'Tamaño_empr_2', 'CARACTER_OCUP', 'JERARQUIA_OCUP', 'TECNOLOGIA_OCUP', 'CALIFICACION_OCUP', 'ACTIV_ECON', 'CAT_ECON'] Location of categorical columns : [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
Pool Object
La función Pool en Catboost combina variables independientes y dependientes (X e y), así como también variables categóricas. Le pasaremos el Pool Object como la data de entrenamiento al método fit(). No necesitamos aplicar OneHotEncoding, dado que cuando construimos el modelo, el objeto pool ya tiene esa información. Crearemos el objeto pool empelando el siguiente código.
# Importamos Pool
from catboost import Pool
#Creamos el objeto pool para el dataset de entrenamiento. Le damos información sobre las columnas
#categóricas al parámetro cat_features
train_data = Pool(data=X2,
label=y2,
cat_features=cat_features
)
#Construimos el modelo
cat_model = CatBoostRegressor(loss_function= 'MAE')
#Lo ubicamos en una lista que emplearemos en Cross-Validation
modelos=[cat_model]
#Corremos la función definida en el apartado "Cross-Validation"
cv_metricas, maes, mses, r2s = cv_comparison(modelos, X2, y2, 6)
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cv_metricas.columns
Index(['<catboost.core.CatBoostRegressor object at 0x7f9bdd3ca910>'], dtype='object')
#Renombramos la columna de la tabla de resultados
cv_metricas = cv_metricas.rename(columns={'<catboost.core.CatBoostRegressor object at 0x7f9bdd3ca910>':'CatBoostRegressor'})
#Modificamos el formato de la columna
cv_metricas['CatBoostRegressor'] = cv_metricas['CatBoostRegressor'].map('{:,.4f}'.format)
cv_metricas
| CatBoostRegressor | |
|---|---|
| Mean Absolute Error | 3,552.0000 |
| Mean Squared Error | 96,902,791.0000 |
| R^2 | 0.8316 |
Vemos que el valor del MAE se redujo a $3683 y el R^2 se mantuvo prácticamente igual al del CV realizado cuando se hizo la competencia entre varios modelos.
Realizaremos la selección de los hiperparámetros óptimos para nuestro modelo, empleando el método nativo de Catboost de RandomizedSearch.
train_data = Pool(data=X2,
label=y2,
cat_features=cat_features
)
cat_model = CatBoostRegressor(loss_function= 'MAE')
grid = {'learning_rate': [0.03, 0.1],
'depth': [6, 8, 10],
'l2_leaf_reg': [1, 3, 5, 7, 9],}
randomized_search_results = cat_model.randomized_search(
grid,
train_data,
n_iter=12,
shuffle=False,
)
Queremos conocer cuáles son los hiperparámetros óptimos:
randomized_search_results['params']
{'depth': 8, 'l2_leaf_reg': 3, 'learning_rate': 0.03}
Split de la data.
X_train, X_test, y_train, y_test = train_test_split(X2,y2, test_size=0.30,random_state=42)
Creación del objeto Pool.
#Creamos el objeto pool para el dataset de entrenamiento. Le damos información sobre las columnas
#categóricas al parámetro cat_features
train_data = Pool(data=X_train,
label=y_train,
cat_features=cat_features
)
#Creamos el objeto pool para el dataset de evaluación
test_data = Pool(data=X_test,
label=y_test,
cat_features=cat_features
)
Creación y entrenamiento del modelo.
#Construimos el modelo con los hiperparámetros óptimos
cat_model = CatBoostRegressor(loss_function= 'MAE', depth= 8, l2_leaf_reg=3, learning_rate= 0.03 )
# Entrenamos el modelo
cat_model.fit( X_train, y_train,
eval_set=(X_test, y_test),
)
# Creamos el dataset de feature importances
df_feature_importance = pd.DataFrame(cat_model.get_feature_importance(prettified=True))
#Graficamos feature importances
plt.figure(figsize=(12, 6));
feature_plot= sns.barplot(x="Importances", y="Feature Id", data=df_feature_importance,palette="cool");
plt.title('features importance');
Del gráfico observamos que las tres variables más importantes resultan ser Horas semanales trabajadas (recordemos que del EDA no pudimos obtener una conclusión definitiva sobre ésta), Región de residencia y Tamaño de la empresa, seguida por la edad del trabajador.
Por su parte, la variable Estado (si la persona tiene trabajo o no) resultó ser de importancia nula. Las siguientes variables menos importantes son Jerarquía ocupacional (Si la persona es Directivo, Jefe, Asalariado o Cuentapropista) y el tamaño del aglomerado urbano de residencia (si tiene más de 500 mil habitantes o no).
#Calculamos las predicciones
y_predict= cat_model.predict(X_test)
Reemplazamos los valores negativos de las predicciones:
#Reemplazamos por 0 las predicciones negativas
y_predict = np.where((y_predict <0 ), 0, y_predict)
Creamos una tabla para graficar las predicciones vs. los valores reales de ingresos:
tabla_resumen = X_test.copy()
tabla_resumen['Data real'] = y_test
#Añadimos las predicciones del modelo final a nuestra tabla
tabla_resumen['Predicciones'] = y_predict
tabla_resumen.sample(10)
| REGION | AGLOMERADO | MAS_500 | Sexo | Edad | NIVEL_ED_2 | NIVEL_ED | ESTADO | CAT_OCUP | Cant_Ocup | ... | Lugar_trab | Tamaño_empr_2 | CARACTER_OCUP | JERARQUIA_OCUP | TECNOLOGIA_OCUP | CALIFICACION_OCUP | ACTIV_ECON | CAT_ECON | Data real | Predicciones | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12416 | 5 | 33 | 1 | 2 | 47 | 6 | 5 | 1 | 2 | 1 | ... | 9 | 0 | 34 | 2 | 2 | 2 | 49 | 13 | 70000 | 6.695844e+04 |
| 26578 | 2 | 12 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 1.379172e-08 |
| 37979 | 4 | 4 | 1 | 1 | 30 | 7 | 5 | 1 | 2 | 1 | ... | 1 | 3 | 10 | 2 | 3 | 2 | 85 | 16 | 60000 | 4.574408e+04 |
| 36525 | 2 | 15 | 0 | 1 | 52 | 7 | 5 | 1 | 2 | 1 | ... | 1 | 3 | 10 | 2 | 3 | 2 | 85 | 16 | 45000 | 5.329982e+04 |
| 31307 | 4 | 13 | 1 | 2 | 22 | 6 | 5 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 3.226277e-07 |
| 46744 | 4 | 6 | 0 | 2 | 67 | 2 | 2 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0.000000e+00 |
| 25970 | 4 | 30 | 0 | 1 | 94 | 2 | 2 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 6.199677e-07 |
| 31988 | 6 | 17 | 0 | 1 | 51 | 7 | 5 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 4.311578e-07 |
| 23088 | 2 | 7 | 0 | 1 | 75 | 2 | 2 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 6.817705e-08 |
| 7980 | 1 | 29 | 1 | 1 | 13 | 2 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0.000000e+00 |
10 rows × 26 columns
Graficamos:
fig = px.scatter(tabla_resumen,
x= tabla_resumen.index,
y= ['Data real','Predicciones'],
trendline="lowess",
trendline_options=dict(frac=0.03),
title="Ingresos en miles de pesos argentinos - Data real vs Predicciones")
fig.data = [t for t in fig.data if t.mode == "lines"]
fig.update_traces(showlegend=True) #trendlines have showlegend=False by default
fig.show()
Métricas finales
import math
#RMSE
Rmse_test = math.sqrt(mean_squared_error(y_test,y_predict))
#R2 Score
r2_test = r2_score(y_test,y_predict)
# Adjusted R2 Score
n= X_train.shape[0] # total num de datapoints
p= X_train.shape[1] # total num de features independientes
adj_r2_test = 1-(1-r2_test)*(n-1)/(n-p-1)
#Mean absolute error
MAE_test= mean_absolute_error(y_test, y_predict)
#Imprimimos resultados
print("Evaluation on test data")
print("RMSE: {:.2f}".format(Rmse_test))
print("R2: {:.3f}".format(r2_test))
print("Adjusted R2: {:.3f}".format(adj_r2_test))
print('MAE: {:.2f}'.format(MAE_test))
Evaluation on test data RMSE: 9763.16 R2: 0.831 Adjusted R2: 0.831 MAE: 3471.25
Del gráfico comparativo Data Real vs. Predicciones, observamos que nuestro modelo predice algunos picos erróneamente. Sin embargo, el Error Absoluto Medio logró reducirse a $3582, lo que equivale a unos 31 dólares estadounidenses, al valor de cambio oficial de Marzo 2022, fecha del dataset. Por su parte, el R^2 logró aumentarse a 0,84.
Recordando que el ingreso promedio es USD 476, el margen de error promedio sería de +-USD 15.5, lo que representa un error promedio del 3.25% en las predicciones.
Las predicciones de este modelo se basan en los datos de ingresos recabados en un solo trimestre del año 2022.
A fin de confirmar y/o revisar los insights obtenidos, sería bueno extender el análisis al resto de los trimestres y años disponibles (al menos 5 años hacia atrás). Además, esto daría una idea de la evolución del salario en el tiempo, dado el fenómeno inflacionario que padece Argentina.
Otro punto interesante sería combinar la base de datos “Personas” (empleada en este proyecto) con la de “Hogares”, para analizar reglas de asociación entre ellas y establecer patrones de comportamiento.